Methods

Packages & Data

Packages

> # Attaching packages (red = needs update)
> <U+26A0> insight     0.8.1.1   <U+2714> bayestestR  0.5.2  
> <U+2714> performance 0.4.4.1   <U+2714> parameters  0.5.0.1
> <U+26A0> see         0.4.0.1   <U+2714> effectsize  0.2.0  
> <U+26A0> modelbased  0.1.0     <U+2714> correlation 0.1.0  
> <U+2714> report      0.1.0     
> Warnings or errors in CRAN checks for package(s) 'bayestestR', 'parameters'.
> Restart the R-Session and update packages in red with 'easystats::easystats_update()'.

Preprocessing

> [1] "The initial sample included 1011 participants (Mean age = 25.56, SD = 7.89, range = [12.96, 73.51]; 55.09% females; Mean education = 3.38, SD = 2.15, range = [-7, 10])"

Measures Scoring

Deception and Lying Profile (LIE)

We rescaled the LIE variables, originally scored on a -10 to 10 scale, to -5 to 5, so that the coefficients are more easily interpretable (i.e., refers to a change of 10% of the scale).

Psychopathy (TRIMP)

df_raw <- df_raw %>%
   # Transform to numeric
  mutate_at(vars(starts_with("TRIMP")), function(x) {
    ifelse(x == "TRUE", 3,
           ifelse(x == "somewhat true", 2,
                  ifelse(x == "somewhat false", 1, 0)))
    }) %>%
  # Reverse items
  mutate_at(vars("TRIMP_2", "TRIMP_4", "TRIMP_10", "TRIMP_11", "TRIMP_16", "TRIMP_21", "TRIMP_25", "TRIMP_30", "TRIMP_33", "TRIMP_35", "TRIMP_39", "TRIMP_41", "TRIMP_44", "TRIMP_47", "TRIMP_50", "TRIMP_52", "TRIMP_57"), reverse, mini = 0, maxi = 3) %>% 
  # Compute scores
      ## Boldness
  mutate(
    TRIMP_Boldness = (TRIMP_1 + TRIMP_16
                      + TRIMP_7 + TRIMP_32
                      + TRIMP_10 + TRIMP_28
                      + TRIMP_13 + TRIMP_41
                      + TRIMP_19 + TRIMP_38 + TRIMP_57
                      + TRIMP_4 + TRIMP_47 
                      + TRIMP_22 + TRIMP_35
                      + TRIMP_25 + TRIMP_50
                      + TRIMP_44 + TRIMP_54)/19,
    TRIMP_Boldness_Optimism = (TRIMP_1 + TRIMP_16)/2,
    TRIMP_Boldness_Resilience = (TRIMP_7 + TRIMP_32)/2,
    TRIMP_Boldness_Courage = (TRIMP_10 + TRIMP_28)/2,
    TRIMP_Boldness_Dominance = (TRIMP_13 + TRIMP_41)/2,
    TRIMP_Boldness_Persuasiveness = (TRIMP_19 + TRIMP_38 + TRIMP_57)/3,
    TRIMP_Boldness_Intrepidness = (TRIMP_4 + TRIMP_47)/2,
    TRIMP_Boldness_ToleranceForUncertainty = (TRIMP_22 + TRIMP_35)/2,
    TRIMP_Boldness_SelfConfidence = (TRIMP_25 + TRIMP_50)/2,
    TRIMP_Boldness_SocialAssurance = (TRIMP_44 + TRIMP_54)/2
) %>% 
      ## Meanness
  mutate(
    TRIMP_Meanness = (TRIMP_2 + TRIMP_8 + TRIMP_11 + TRIMP_20 + TRIMP_29 + TRIMP_33 + TRIMP_36 + TRIMP_48 + TRIMP_52 + TRIMP_55 
                      + TRIMP_6 + TRIMP_45
                      + TRIMP_14
                      + TRIMP_17 + TRIMP_23 + TRIMP_26 + TRIMP_42
                      + TRIMP_39
                      + TRIMP_40)/19,
    TRIMP_Meanness_Empathy = (TRIMP_2 + TRIMP_8 + TRIMP_11 + TRIMP_20 + TRIMP_29 + TRIMP_33 + TRIMP_36 + TRIMP_48 + TRIMP_52 + TRIMP_55)/10,
    TRIMP_Meanness_ExcitementSeeking = (TRIMP_6 + TRIMP_45)/2,
    TRIMP_Meanness_PhysicalAggression = TRIMP_14,
    TRIMP_Meanness_RelationalAggression = (TRIMP_17 + TRIMP_23 + TRIMP_26 + TRIMP_42)/4,
    TRIMP_Meanness_Honesty = TRIMP_39,
    TRIMP_Meanness_DestructiveAggression = TRIMP_40
) %>% 
     ## Disinhibition
  mutate(
    TRIMP_Disinhibition = (
      TRIMP_3 + TRIMP_46 + 
        TRIMP_5 + TRIMP_30 + 
        TRIMP_9 + TRIMP_15 + TRIMP_37 + TRIMP_51 +
        TRIMP_12 + TRIMP_18 + TRIMP_49 + TRIMP_56 +
        TRIMP_21 +
        TRIMP_24 + TRIMP_43 + TRIMP_53 + TRIMP_58 +
        TRIMP_27 +
        TRIMP_31 +
        TRIMP_34)/20,
    TRIMP_Disinhibition_ImpatienceUrgency = (TRIMP_3 + TRIMP_46)/2,
    TRIMP_Disinhibition_Dependability = (TRIMP_5 + TRIMP_30)/2,
    TRIMP_Disinhibition_ProblematicImpulsivity = (TRIMP_9 + TRIMP_15 + TRIMP_37 + TRIMP_51)/4,
    TRIMP_Disinhibition_Irresponsibility = (TRIMP_12 + TRIMP_18 + TRIMP_49 + TRIMP_56)/4,
    TRIMP_Disinhibition_PlanfulControl = TRIMP_21,
    TRIMP_Disinhibition_Theft = (TRIMP_24 + TRIMP_43 + TRIMP_53 + TRIMP_58)/4,
    TRIMP_Disinhibition_Alienation = TRIMP_27,
    TRIMP_Disinhibition_BoredomProneness = TRIMP_31,
    TRIMP_Disinhibition_Fraud = TRIMP_34
) %>% 
  ## General
      mutate(TRIMP_General = (TRIMP_Boldness*19 + TRIMP_Meanness*19 + TRIMP_Disinhibition*20)/58
) %>%
  # Remove individual questions
  select(-matches("TRIMP_\\d"))

Data Exclusion

Multivariate outliers

methods <- c("zscore", "iqr", "mahalanobis", "robust", "mcd", "ics", "iforest", "lof")

# outliers <- df_time %>%
#   select(matches("LIE_|BIDR|IPIP6|PID5|TRIMP|FFNI|UPPS|DERS|LTS|MAIA"), -matches("_Profile|_General|_Pathology|Disinhibition_|Meanness_|Boldness_")) %>%
#   # select(matches("LIE_")) %>%
#   effectsize::standardize() %>%
#   performance::check_outliers(method = methods)
# 
# # Visualise
# as.data.frame(outliers) %>% 
#   mutate(Outlier = as.factor(paste0(round(Outlier*8), "/", length(methods)))) %>% 
#   ggplot(aes(x = Outlier, fill = Outlier)) +
#   geom_bar() +
#   geom_vline(aes(xintercept = 6.5), color = "red", linetype = "dotted") +
#   theme_modern() +
#   see::scale_fill_metro_d(guide = FALSE) +
#   xlab("Proportion of methods aggreeing on an outlier") +
#   ylab("Number of participants")
# 
# save(outliers, file="outliers.Rdata")
load("outliers.Rdata")

df <- df_time[-which(as.numeric(outliers) >= 6/length(methods)), ]

paste("Based on a composite outlier score (see the 'check_outliers' function in the 'performance' R package; Lüdecke et al., 2019) obtained via the joint application of multiple outliers detection algorithms (Z-scores, Iglewicz, 1993; Interquartile range (IQR); Mahalanobis distance, Cabana, 2019; Robust Mahalanobis distance, Gnanadesikan & Kettenring, 1972; Minimum Covariance Determinant, Leys et al., 2018; Invariant Coordinate Selection, Archimbaud et al., 2018; Isolation Forest, Liu et al. 2008; and Local Outlier Factor, Breunig et al., 2000), we excluded", nrow(df_time) - nrow(df), "participants that were classified as outliers by at least 6/8 of the methods used.")
> [1] "Based on a composite outlier score (see the 'check_outliers' function in the 'performance' R package; Lüdecke et al., 2019) obtained via the joint application of multiple outliers detection algorithms (Z-scores, Iglewicz, 1993; Interquartile range (IQR); Mahalanobis distance, Cabana, 2019; Robust Mahalanobis distance, Gnanadesikan & Kettenring, 1972; Minimum Covariance Determinant, Leys et al., 2018; Invariant Coordinate Selection, Archimbaud et al., 2018; Isolation Forest, Liu et al. 2008; and Local Outlier Factor, Breunig et al., 2000), we excluded 103 participants that were classified as outliers by at least 6/8 of the methods used."

Final Sample

> [1] "The final sample included 762 participants (Mean age = 25.42, SD = 7.76, range = [19.02, 73.51]; 56.04% females; Mean education = 3.51, SD = 2.00, range = [-7, 10])"
> The data contains 762 observations of the following variables:
>   - System_Device: 3 entries: Phone, 74.15%; Computer, 25.07%; Tablet, 0.79%(0.00% missing)
>   - System_Screen: Mean = 692.93, SD = 265.01, Median = 552.00, MAD = 76.91, range: [426.33, 2225.67], Skewness = 1.50, Kurtosis = 1.63, 0% missing
>   - Duration: Mean = 23.90, SD = 9.69, Median = 21.23, MAD = 7.12, range: [10.97, 61.70], Skewness = 1.42, Kurtosis = 2.04, 0% missing
>   - Education_Student: 2 levels: No (n = 229, 30.05%); Yes (n = 532, 69.82%) and missing (n = 1, 0.13%)
>   - Education_Type: 18 entries: Business and Accountancy, 22.31%; Engineering, 19.29%; Social Sciences (Psychology, Sociology, etc.), 15.88%; Sciences, 10.89%; Others, 7.22%; Computing, 5.77%; Humanities (Languages, History, etc.), 4.99%; Communication Studies, 3.28%; Medicine, 3.02%; Art and Design, 1.97% and 8 others(0.00% missing)
>   - Ethnicity: 20 entries: Chinese, 87.80%; Malay, 4.33%; Indian, 3.41%; , 1.84%; Vietnamese, 0.39%; Eurasian, 0.26%; Korean, 0.26%; African, 0.13%; Arabic, 0.13%; Boyanese , 0.13% and 10 others(0.00% missing)
>   - Religion_Type: 6 entries: Buddhism, 33.33%; No religion, 25.46%; Christianity, 24.80%; Taoism, 5.77%; Islam, 4.99%; Hinduism, 2.36%(3.28% missing)
>   - Religion_Religiosity: Mean = 4.22, SD = 2.97, Median = , MAD = 4.45, range: [0, 10], Skewness = 0.06, Kurtosis = -1.26, 2.89% missing
>   - Religion_Engagement: Mean = 3.82, SD = 3.09, Median = , MAD = 4.45, range: [0, 10], Skewness = 0.30, Kurtosis = -1.18, 3.54% missing
>   - Income: Mean = 2802.21, SD = 4348.98, Median = , MAD = 1482.60, range: [0, 60000], Skewness = 8.46, Kurtosis = 96.78, 13.25% missing
>   - Singapore_Duration: Mean = 0.88, SD = 0.25, Median = , MAD = 0.02, range: [0, 1.00], Skewness = -2.51, Kurtosis = 4.85, 24.41% missing

Education and Income

> [1] "For the 'Sex - Female and Education_Student - No' group: 136 participants (Mean age = 31.28, SD = 11.67, range = [21.73, 73.51]; 100.00% females; Mean education = 2.80, SD = 2.71, range = [-7, 6]), for the 'Sex - Male and Education_Student - No' group: 93 participants (Mean age = 30.88, SD = 11.86, range = [19.73, 66.19]; 0.00% females; Mean education = 2.11, SD = 2.77, range = [-6, 10]), for the 'Sex - Female and Education_Student - Yes' group: 291 participants (Mean age = 22.66, SD = 3.05, range = [19.11, 56.25]; 100.00% females; Mean education = 3.97, SD = 1.45, range = [0, 10]) and for the 'Sex - Male and Education_Student - Yes' group: 241 participants (Mean age = 23.31, SD = 2.06, range = [19.02, 39.79]; 0.00% females; Mean education = 3.88, SD = 1.29, range = [-2, 10])"

Results

Descriptive Statistics

Deception and Lying Profile (LIE)

> Variable |  Mean |   SD | Min | Max | Skewness | Kurtosis
> ---------------------------------------------------------
> LIE_1    | -1.78 | 2.41 |  -5 |   5 |     0.49 |    -0.66
> LIE_2    | -1.40 | 2.54 |  -5 |   5 |     0.37 |    -0.82
> LIE_3    | -2.44 | 2.64 |  -5 |   5 |     1.03 |     0.21
> LIE_4    | -1.61 | 2.46 |  -5 |   5 |     0.40 |    -0.72
> LIE_5    | -1.84 | 2.29 |  -5 |   5 |     0.49 |    -0.58
> LIE_6    | -1.00 | 2.58 |  -5 |   5 |     0.17 |    -1.01
> LIE_7    | -0.78 | 2.70 |  -5 |   5 |     0.17 |    -0.97
> LIE_8    | -1.37 | 2.29 |  -5 |   5 |     0.41 |    -0.43
> LIE_9    | -0.20 | 2.74 |  -5 |   5 |    -0.15 |    -0.98
> LIE_10   |  0.13 | 2.72 |  -5 |   5 |    -0.27 |    -0.88
> LIE_11   | -0.37 | 2.62 |  -5 |   5 |    -0.04 |    -0.91
> LIE_12   |  0.77 | 2.64 |  -5 |   5 |    -0.56 |    -0.52
> LIE_13   | -0.46 | 2.56 |  -5 |   5 |     0.25 |    -0.68
> LIE_14   |  0.42 | 2.46 |  -5 |   5 |    -0.38 |    -0.58
> LIE_15   |  0.16 | 2.61 |  -5 |   5 |    -0.15 |    -0.83
> LIE_16   | -1.00 | 2.39 |  -5 |   5 |     0.38 |    -0.41
> LIE_17   |  0.56 | 2.71 |  -5 |   5 |    -0.07 |    -0.97
> LIE_18   | -0.02 | 2.67 |  -5 |   5 |    -0.23 |    -0.93
> LIE_19   |  1.02 | 2.64 |  -5 |   5 |    -0.35 |    -0.83
> LIE_20   | -0.18 | 2.61 |  -5 |   5 |    -0.06 |    -0.86
> LIE_21   | -1.01 | 2.75 |  -5 |   5 |     0.36 |    -0.84
> LIE_22   | -2.26 | 2.35 |  -5 |   5 |     0.67 |    -0.43
> LIE_23   | -1.87 | 2.37 |  -5 |   5 |     0.61 |    -0.38
> LIE_24   |  1.24 | 2.36 |  -5 |   5 |    -0.39 |    -0.48
> LIE_25   |  1.73 | 2.41 |  -5 |   5 |    -0.62 |    -0.22
> LIE_26   | -2.61 | 2.20 |  -5 |   5 |     0.89 |     0.15
> LIE_27   |  1.15 | 2.58 |  -5 |   5 |    -0.40 |    -0.70
> LIE_28   | -0.26 | 2.56 |  -5 |   5 |    -0.10 |    -0.76
> LIE_29   | -0.96 | 2.69 |  -5 |   5 |     0.28 |    -0.91
> LIE_30   |  0.39 | 2.69 |  -5 |   5 |    -0.33 |    -0.84
> LIE_31   | -0.42 | 2.62 |  -5 |   5 |    -0.04 |    -0.96
> LIE_32   |  0.53 | 2.67 |  -5 |   5 |    -0.37 |    -0.74
> LIE_33   |  1.84 | 2.18 |  -5 |   5 |    -1.05 |     1.33
> LIE_34   |  2.64 | 2.00 |  -5 |   5 |    -0.76 |     0.31
> LIE_35   |  1.73 | 2.31 |  -5 |   5 |    -0.59 |    -0.14
> LIE_36   |  1.02 | 2.41 |  -5 |   5 |    -0.25 |    -0.65
> LIE_37   |  0.56 | 2.73 |  -5 |   5 |    -0.01 |    -0.97
> LIE_38   |  1.81 | 2.51 |  -5 |   5 |    -0.55 |    -0.60
> LIE_39   |  1.69 | 2.46 |  -5 |   5 |    -0.87 |     0.30
> LIE_40   |  1.80 | 2.42 |  -5 |   5 |    -0.73 |     0.06
> LIE_41   |  0.99 | 2.65 |  -5 |   5 |    -0.19 |    -0.83
> LIE_42   |  1.87 | 2.22 |  -5 |   5 |    -0.76 |     0.58
> LIE_43   |  1.78 | 2.28 |  -5 |   5 |    -0.99 |     0.98
> LIE_44   |  2.05 | 2.39 |  -5 |   5 |    -0.67 |    -0.14

Psychopathy (TRIMP)

> Variable                                   | Mean |   SD |  Min |  Max | Skewness | Kurtosis
> --------------------------------------------------------------------------------------------
> TRIMP_General                              | 1.08 | 0.28 | 0.34 | 1.90 |     0.25 |    -0.30
> TRIMP_Boldness                             | 1.43 | 0.37 | 0.26 | 2.37 |    -0.15 |    -0.01
> TRIMP_Boldness_Optimism                    | 1.72 | 0.56 | 0.00 | 3.00 |    -0.31 |     0.20
> TRIMP_Boldness_Resilience                  | 1.55 | 0.63 | 0.00 | 3.00 |    -0.13 |    -0.28
> TRIMP_Boldness_Courage                     | 1.36 | 0.67 | 0.00 | 3.00 |     0.08 |    -0.06
> TRIMP_Boldness_Dominance                   | 1.39 | 0.70 | 0.00 | 3.00 |    -0.02 |    -0.30
> TRIMP_Boldness_Persuasiveness              | 1.53 | 0.59 | 0.00 | 3.00 |    -0.18 |    -0.18
> TRIMP_Boldness_Intrepidness                | 1.06 | 0.73 | 0.00 | 3.00 |     0.32 |    -0.59
> TRIMP_Boldness_ToleranceForUncertainty     | 1.32 | 0.60 | 0.00 | 3.00 |     0.09 |     0.26
> TRIMP_Boldness_SelfConfidence              | 1.58 | 0.66 | 0.00 | 3.00 |     0.11 |    -0.35
> TRIMP_Boldness_SocialAssurance             | 1.31 | 0.65 | 0.00 | 3.00 |     0.06 |    -0.09
> TRIMP_Meanness                             | 0.88 | 0.40 | 0.00 | 2.05 |     0.24 |    -0.44
> TRIMP_Meanness_Empathy                     | 0.80 | 0.45 | 0.00 | 2.10 |     0.23 |    -0.64
> TRIMP_Meanness_ExcitementSeeking           | 1.30 | 0.79 | 0.00 | 3.00 |     0.02 |    -0.68
> TRIMP_Meanness_PhysicalAggression          | 0.88 | 0.92 | 0.00 | 3.00 |     0.61 |    -0.77
> TRIMP_Meanness_RelationalAggression        | 1.02 | 0.63 | 0.00 | 2.75 |     0.23 |    -0.62
> TRIMP_Meanness_Honesty                     | 0.79 | 0.69 | 0.00 | 3.00 |     0.61 |     0.33
> TRIMP_Meanness_DestructiveAggression       | 0.35 | 0.63 | 0.00 | 3.00 |     1.70 |     1.96
> TRIMP_Disinhibition                        | 0.93 | 0.40 | 0.05 | 2.30 |     0.44 |    -0.08
> TRIMP_Disinhibition_ImpatienceUrgency      | 1.74 | 0.59 | 0.00 | 3.00 |    -0.30 |     0.11
> TRIMP_Disinhibition_Dependability          | 0.92 | 0.65 | 0.00 | 3.00 |     0.25 |    -0.46
> TRIMP_Disinhibition_ProblematicImpulsivity | 1.11 | 0.63 | 0.00 | 2.75 |     0.03 |    -0.66
> TRIMP_Disinhibition_Irresponsibility       | 0.65 | 0.60 | 0.00 | 2.50 |     0.77 |    -0.25
> TRIMP_Disinhibition_PlanfulControl         | 1.09 | 0.64 | 0.00 | 3.00 |     0.45 |     0.74
> TRIMP_Disinhibition_Theft                  | 0.39 | 0.52 | 0.00 | 2.50 |     1.49 |     1.72
> TRIMP_Disinhibition_Alienation             | 1.45 | 0.87 | 0.00 | 3.00 |     0.00 |    -0.67
> TRIMP_Disinhibition_BoredomProneness       | 1.70 | 0.81 | 0.00 | 3.00 |    -0.22 |    -0.40
> TRIMP_Disinhibition_Fraud                  | 0.40 | 0.69 | 0.00 | 3.00 |     1.64 |     1.89

Narcissism (FFNI)

> Variable               |  Mean |   SD |  Min |   Max | Skewness | Kurtosis
> --------------------------------------------------------------------------
> FFNI_AcclaimSeeking    | 14.19 | 3.33 | 4.00 | 20.00 |    -0.62 |     0.41
> FFNI_Distrust          | 12.27 | 2.83 | 4.00 | 20.00 |    -0.04 |    -0.02
> FFNI_Entitlement       |  9.94 | 3.63 | 4.00 | 20.00 |     0.14 |    -0.77
> FFNI_Exploitativeness  |  9.01 | 3.67 | 4.00 | 20.00 |     0.34 |    -0.80
> FFNI_Indifference      | 11.03 | 3.75 | 4.00 | 20.00 |     0.22 |    -0.63
> FFNI_LackOfEmpathy     |  9.19 | 3.14 | 4.00 | 20.00 |     0.42 |    -0.38
> FFNI_Manipulativeness  | 10.31 | 3.66 | 4.00 | 20.00 |     0.13 |    -0.78
> FFNI_NeedForAdmiration | 12.87 | 2.89 | 4.00 | 20.00 |    -0.39 |     0.18
> FFNI_ThrillSeeking     | 10.56 | 3.75 | 4.00 | 20.00 |     0.07 |    -0.78
> FFNI_General           | 11.04 | 1.95 | 5.89 | 19.44 |     0.19 |     0.32

Normal Personality (IPIP6)

> Variable                | Mean |   SD |  Min |  Max | Skewness | Kurtosis
> -------------------------------------------------------------------------
> IPIP6_Extraversion      | 3.58 | 1.20 | 1.00 | 6.75 |     0.13 |    -0.56
> IPIP6_Agreableness      | 4.99 | 0.90 | 1.25 | 7.00 |    -0.29 |     0.11
> IPIP6_Conscientiousness | 4.48 | 1.07 | 1.00 | 7.00 |    -0.24 |     0.00
> IPIP6_Neuroticism       | 3.91 | 1.09 | 1.00 | 7.00 |    -0.02 |    -0.21
> IPIP6_Openeness         | 4.53 | 1.05 | 1.50 | 7.00 |    -0.10 |    -0.38
> IPIP6_HonestyHumility   | 4.39 | 1.24 | 1.25 | 7.00 |    -0.04 |    -0.60

Pathological Personality (PID-5)

> Variable            | Mean |   SD | Min | Max | Skewness | Kurtosis
> -------------------------------------------------------------------
> PID5_NegativeAffect | 1.40 | 0.63 |   0 |   3 |    -0.13 |    -0.36
> PID5_Detachment     | 1.08 | 0.55 |   0 |   3 |     0.10 |    -0.17
> PID5_Antagonism     | 0.89 | 0.54 |   0 |   3 |     0.38 |    -0.10
> PID5_Disinhibition  | 0.97 | 0.63 |   0 |   3 |     0.15 |    -0.77
> PID5_Psychoticism   | 1.24 | 0.60 |   0 |   3 |    -0.09 |    -0.31
> PID5_Pathology      | 1.12 | 0.45 |   0 |   3 |    -0.02 |     0.12

Social Desirability (BIDR)

> Variable                      | Mean |   SD |  Min |  Max | Skewness | Kurtosis
> -------------------------------------------------------------------------------
> BIDR_SelfDeceptiveEnhancement | 3.90 | 0.80 | 1.50 | 7.00 |     0.09 |     0.45
> BIDR_ImpressionManagement     | 3.82 | 0.81 | 1.12 | 6.62 |     0.21 |     0.51
> BIDR_General                  | 3.86 | 0.65 | 1.44 | 6.56 |     0.31 |     1.09

Impulsivity (UPPS)

> Variable                 | Mean |   SD |  Min |  Max | Skewness | Kurtosis
> --------------------------------------------------------------------------
> UPPS_NegativeUrgency     | 2.39 | 0.62 | 1.00 | 4.00 |    -0.22 |    -0.34
> UPPS_PositiveUrgency     | 2.13 | 0.61 | 1.00 | 3.75 |    -0.09 |    -0.61
> UPPS_LackOfPerseverance  | 1.85 | 0.44 | 1.00 | 3.25 |    -0.02 |    -0.14
> UPPS_LackOfPremeditation | 1.87 | 0.44 | 1.00 | 3.75 |    -0.06 |     0.34
> UPPS_SensationSeeking    | 2.68 | 0.65 | 1.00 | 4.00 |    -0.24 |    -0.26
> UPPS_General             | 2.18 | 0.34 | 1.10 | 3.00 |    -0.40 |    -0.17

Emotion Regulation (DERS)

> Variable           | Mean |   SD | Min | Max | Skewness | Kurtosis
> ------------------------------------------------------------------
> DERS_Awareness     | 7.04 | 2.01 |   3 |  13 |     0.33 |    -0.18
> DERS_Clarity       | 7.42 | 2.46 |   3 |  15 |     0.44 |    -0.18
> DERS_Goals         | 9.36 | 3.16 |   3 |  15 |     0.00 |    -0.94
> DERS_Impulse       | 6.76 | 2.96 |   3 |  15 |     0.58 |    -0.48
> DERS_NonAcceptance | 7.32 | 3.04 |   3 |  15 |     0.49 |    -0.55
> DERS_Strategies    | 6.92 | 2.99 |   3 |  15 |     0.55 |    -0.56
> DERS_General       | 7.47 | 1.96 |   3 |  13 |     0.26 |    -0.57

Light Triad (LTS)

> Variable            | Mean |   SD | Min |  Max | Skewness | Kurtosis
> --------------------------------------------------------------------
> LTS_FaithInHumanity | 2.38 | 0.62 |   1 | 4.50 |     0.54 |     0.54
> LTS_Humanism        | 2.04 | 0.49 |   1 | 4.00 |     0.28 |     0.47
> LTS_Kantianism      | 2.08 | 0.56 |   1 | 4.00 |     0.35 |     0.16
> LTS_General         | 2.16 | 0.44 |   1 | 4.00 |     0.24 |     0.50

Interoception (MAIA2)

> Variable            | Mean |   SD | Min | Max | Skewness | Kurtosis
> -------------------------------------------------------------------
> MAIA2_Noticing      | 3.15 | 0.85 |   0 |   5 |    -0.44 |     0.22
> MAIA2_BodyListening | 2.82 | 0.89 |   0 |   5 |    -0.40 |    -0.06

Factor Structure

Factor structure quality

> # Is the data suitable for Factor Analysis?
> 
>   - KMO: The Kaiser, Meyer, Olkin (KMO) measure of sampling adequacy suggests that data seems appropriate for factor analysis (KMO = 0.95).
>   - Sphericity: Bartlett's test of sphericity suggests that there is sufficient significant correlation in the data for factor analaysis (Chisq(946) = 16257.03, p < .001).

How many factors

> # Method Agreement Procedure:
> 
> The choice of 7 dimensions is supported by 5 (20.00%) methods out of 25 (t, p, Optimal coordinates, Parallel analysis, Kaiser criterion).

Exploratory Factor Analysis (EFA)

Seven latent factors model

> # Rotated loadings from Factor Analysis (varimax-rotation)
> 
> Variable |                                                      Label |   ML1 |   ML2 |  ML4 |  ML3 |  ML6 |  ML5 |  ML7 | Complexity | Uniqueness
> --------------------------------------------------------------------------------------------------------------------------------------------------
> LIE_10   |                                             I can lie well |  0.85 |       |      |      |      |      |      |       1.30 |       0.17
> LIE_9    |                                           I am a good liar |  0.81 |       |      |      |      |      |      |       1.36 |       0.23
> LIE_14   |                    It is hard for others to detect my lies |  0.75 |       |      |      |      |      |      |       1.18 |       0.38
> LIE_18   |                   It is easy for me to make up clever lies |  0.75 |       |      |      |      |      |      |       1.44 |       0.32
> LIE_11   |                              I am good at deceiving others |  0.74 |       |      |      |      |      |      |       1.42 |       0.34
> LIE_12   |                         I can lie effectively if I want to |  0.73 |       |      |      |      |      |      |       1.33 |       0.38
> LIE_13   |                      Others can easily tell when I’m lying | -0.69 |       |      |      |      |      |      |       1.44 |       0.42
> LIE_17   |                                     I find lying difficult | -0.63 |       |      |      |      |      |      |       2.07 |       0.39
> LIE_15   |                            I almost never get caught lying |  0.62 |       |      |      |      |      |      |       1.26 |       0.56
> LIE_20   |                    I do not have to prepare much for a lie |  0.54 |       |      |      |      |      |      |       2.09 |       0.56
> LIE_16   |                 My lies often arouse suspicion from others | -0.47 |       |      |      |      |      |      |       2.51 |       0.59
> LIE_27   |                        I feel tense whenever I have to lie | -0.46 |       |      |      |      |      |      |       2.49 |       0.52
> LIE_4    |                                   I have a tendency to lie |       |  0.76 |      |      |      |      |      |       1.31 |       0.34
> LIE_5    |                      I lie more often than most people do  |       |  0.72 |      |      |      |      |      |       1.45 |       0.38
> LIE_7    |                           I lie more than I think I should |       |  0.71 |      |      |      |      |      |       1.29 |       0.42
> LIE_6    |         I lie more frequently than what I expect myself to |       |  0.71 |      |      |      |      |      |       1.24 |       0.44
> LIE_23   |           I find it difficult to refrain myself from lying |       |  0.71 |      |      |      |      |      |       1.18 |       0.45
> LIE_1    |                                           I lie frequently |       |  0.71 |      |      |      |      |      |       1.66 |       0.34
> LIE_22   |                     I find myself lying without any reason |       |  0.67 |      |      |      |      |      |       1.45 |       0.46
> LIE_2    |                                   I lie in many situations |       |  0.60 |      |      |      |      |      |       2.19 |       0.45
> LIE_26   |                                              I enjoy lying |       |  0.53 |      |      |      |      |      |       3.14 |       0.40
> LIE_8    |                            Others lie less often than I do |       |  0.51 |      |      |      |      |      |       1.37 |       0.69
> LIE_29   |                             I lie whenever it’s convenient |       |  0.49 |      |      |      |      |      |       2.75 |       0.54
> LIE_21   |                          I have to try hard to avoid lying |       |  0.46 |      |      |      |      |      |       1.13 |       0.77
> LIE_24   |                  It is easy to hold back from telling lies |       | -0.38 |      |      |      |      |      |       2.01 |       0.79
> LIE_28   |                I feel satisfied when others believe my lie |       |  0.30 |      |      |      |      |      |       4.62 |       0.69
> LIE_41   |                             Lying is against my principles |       |       | 0.65 |      |      |      |      |       1.64 |       0.46
> LIE_44   |                                          It is bad to lie  |       |       | 0.63 |      |      |      |      |       1.32 |       0.54
> LIE_25   |                                  I feel guilty after lying |       |       | 0.59 |      |      |      |      |       1.66 |       0.54
> LIE_34   |                              I always avoid lying if I can |       |       | 0.56 |      |      |      |      |       2.04 |       0.50
> LIE_36   | I prefer to tell the truth even if it gets me into trouble |       |       | 0.41 |      |      |      |      |       2.72 |       0.70
> LIE_37   |                      I would never lie for trivial matters |       |       | 0.35 |      |      |      |      |       2.79 |       0.78
> LIE_38   |                      I would never lie in serious contexts |       |       | 0.32 |      |      |      |      |       2.30 |       0.83
> LIE_43   |                               It is okay to lie sometimes  |       |       |      | 0.72 |      |      |      |       1.46 |       0.37
> LIE_33   |                                       I lie when necessary |       |       |      | 0.67 |      |      |      |       1.42 |       0.47
> LIE_42   |           It is acceptable to lie depending on the context |       |       |      | 0.65 |      |      |      |       1.35 |       0.51
> LIE_40   |                         I would only lie if it is harmless |       |       |      | 0.44 |      |      |      |       1.45 |       0.76
> LIE_39   |            I would lie if something important was at stake |       |       |      | 0.44 |      |      |      |       1.77 |       0.74
> LIE_35   |                 I would only lie if I have no other choice |       |       |      | 0.41 |      |      |      |       2.05 |       0.73
> LIE_32   |            I lie when telling the truth is too troublesome |       |       |      |      | 0.64 |      |      |       1.79 |       0.43
> LIE_30   |              I lie when it’s easier than telling the truth |       |       |      |      | 0.61 |      |      |       1.76 |       0.50
> LIE_31   |       I lie if it’s the most direct way to get what I want |       |       |      |      | 0.44 |      |      |       3.08 |       0.57
> LIE_3    |                                          I never tell lies |       |       |      |      |      | 0.47 |      |       2.41 |       0.62
> LIE_19   |                I find it taxing to come up with a good lie |       |       |      |      |      |      | 0.54 |       2.53 |       0.34
> 
> The 7 latent factors (varimax rotation) accounted for 48.98% of the total variance of the original data (ML1 = 15.09%, ML2 = 13.94%, ML4 = 7.44%, ML3 = 5.98%, ML6 = 3.30%, ML5 = 1.93%, ML7 = 1.30%).

Four latent factors model

> # Rotated loadings from Factor Analysis (varimax-rotation)
> 
> Variable |                                                      Label |   ML1 |   ML2 |  ML4 |   ML3 | Complexity | Uniqueness
> ------------------------------------------------------------------------------------------------------------------------------
> LIE_10   |                                             I can lie well |  0.84 |       |      |       |       1.30 |       0.19
> LIE_9    |                                           I am a good liar |  0.80 |       |      |       |       1.38 |       0.24
> LIE_14   |                    It is hard for others to detect my lies |  0.75 |       |      |       |       1.20 |       0.39
> LIE_18   |                   It is easy for me to make up clever lies |  0.75 |       |      |       |       1.37 |       0.34
> LIE_11   |                              I am good at deceiving others |  0.74 |       |      |       |       1.41 |       0.34
> LIE_12   |                         I can lie effectively if I want to |  0.73 |       |      |       |       1.34 |       0.38
> LIE_13   |                      Others can easily tell when I’m lying | -0.69 |       |      |       |       1.19 |       0.48
> LIE_17   |                                     I find lying difficult | -0.64 |       |      |       |       1.80 |       0.42
> LIE_15   |                            I almost never get caught lying |  0.62 |       |      |       |       1.27 |       0.57
> LIE_20   |                    I do not have to prepare much for a lie |  0.54 |       |      |       |       1.69 |       0.61
> LIE_19   |                I find it taxing to come up with a good lie | -0.54 |       |      |       |       1.75 |       0.59
> LIE_16   |                 My lies often arouse suspicion from others | -0.47 |       |      |       |       1.77 |       0.69
> LIE_4    |                                   I have a tendency to lie |       |  0.76 |      |       |       1.26 |       0.35
> LIE_5    |                      I lie more often than most people do  |       |  0.72 |      |       |       1.38 |       0.38
> LIE_23   |           I find it difficult to refrain myself from lying |       |  0.72 |      |       |       1.07 |       0.47
> LIE_1    |                                           I lie frequently |       |  0.72 |      |       |       1.57 |       0.34
> LIE_22   |                     I find myself lying without any reason |       |  0.69 |      |       |       1.14 |       0.49
> LIE_6    |         I lie more frequently than what I expect myself to |       |  0.68 |      |       |       1.13 |       0.50
> LIE_7    |                           I lie more than I think I should |       |  0.68 |      |       |       1.09 |       0.52
> LIE_2    |                                   I lie in many situations |       |  0.61 |      |       |       1.96 |       0.46
> LIE_26   |                                              I enjoy lying |       |  0.56 |      |       |       2.01 |       0.52
> LIE_29   |                             I lie whenever it’s convenient |       |  0.53 |      |       |       1.93 |       0.59
> LIE_8    |                            Others lie less often than I do |       |  0.51 |      |       |       1.35 |       0.69
> LIE_21   |                          I have to try hard to avoid lying |       |  0.47 |      |       |       1.12 |       0.77
> LIE_31   |       I lie if it’s the most direct way to get what I want |       |  0.44 |      |       |       2.40 |       0.66
> LIE_24   |                  It is easy to hold back from telling lies |       | -0.36 |      |       |       1.64 |       0.83
> LIE_28   |                I feel satisfied when others believe my lie |       |  0.32 |      |       |       3.43 |       0.72
> LIE_41   |                             Lying is against my principles |       |       | 0.63 |       |       1.69 |       0.47
> LIE_25   |                                  I feel guilty after lying |       |       | 0.59 |       |       1.55 |       0.55
> LIE_44   |                                          It is bad to lie  |       |       | 0.58 |       |       1.33 |       0.61
> LIE_34   |                              I always avoid lying if I can |       |       | 0.57 |       |       1.86 |       0.50
> LIE_27   |                        I feel tense whenever I have to lie |       |       | 0.50 |       |       1.99 |       0.54
> LIE_36   | I prefer to tell the truth even if it gets me into trouble |       |       | 0.43 |       |       2.08 |       0.72
> LIE_37   |                      I would never lie for trivial matters |       |       | 0.33 |       |       1.58 |       0.86
> LIE_38   |                      I would never lie in serious contexts |       |       | 0.32 |       |       1.73 |       0.86
> LIE_43   |                               It is okay to lie sometimes  |       |       |      |  0.71 |       1.33 |       0.42
> LIE_33   |                                       I lie when necessary |       |       |      |  0.70 |       1.14 |       0.47
> LIE_42   |           It is acceptable to lie depending on the context |       |       |      |  0.61 |       1.32 |       0.57
> LIE_39   |            I would lie if something important was at stake |       |       |      |  0.48 |       1.23 |       0.74
> LIE_40   |                         I would only lie if it is harmless |       |       |      |  0.46 |       1.20 |       0.77
> LIE_32   |            I lie when telling the truth is too troublesome |       |       |      |  0.44 |       2.15 |       0.68
> LIE_30   |              I lie when it’s easier than telling the truth |       |       |      |  0.41 |       2.11 |       0.72
> LIE_35   |                 I would only lie if I have no other choice |       |       |      |  0.37 |       2.19 |       0.75
> LIE_3    |                                          I never tell lies |       |       |      | -0.36 |       1.98 |       0.80
> 
> The 4 latent factors (varimax rotation) accounted for 44.18% of the total variance of the original data (ML1 = 14.84%, ML2 = 14.67%, ML4 = 7.52%, ML3 = 7.15%).

One latent factors model

> # Rotated loadings from Factor Analysis (varimax-rotation)
> 
> Variable |                                                      Label |   ML1 | Complexity | Uniqueness
> -------------------------------------------------------------------------------------------------------
> LIE_10   |                                             I can lie well |  0.82 |       1.00 |       0.33
> LIE_9    |                                           I am a good liar |  0.81 |       1.00 |       0.34
> LIE_11   |                              I am good at deceiving others |  0.76 |       1.00 |       0.42
> LIE_18   |                   It is easy for me to make up clever lies |  0.76 |       1.00 |       0.43
> LIE_12   |                         I can lie effectively if I want to |  0.70 |       1.00 |       0.51
> LIE_14   |                    It is hard for others to detect my lies |  0.69 |       1.00 |       0.53
> LIE_17   |                                     I find lying difficult | -0.68 |       1.00 |       0.54
> LIE_2    |                                   I lie in many situations |  0.63 |       1.00 |       0.60
> LIE_1    |                                           I lie frequently |  0.63 |       1.00 |       0.61
> LIE_20   |                    I do not have to prepare much for a lie |  0.61 |       1.00 |       0.62
> LIE_5    |                      I lie more often than most people do  |  0.60 |       1.00 |       0.64
> LIE_4    |                                   I have a tendency to lie |  0.59 |       1.00 |       0.65
> LIE_15   |                            I almost never get caught lying |  0.58 |       1.00 |       0.67
> LIE_26   |                                              I enjoy lying |  0.57 |       1.00 |       0.67
> LIE_13   |                      Others can easily tell when I’m lying | -0.54 |       1.00 |       0.71
> LIE_19   |                I find it taxing to come up with a good lie | -0.53 |       1.00 |       0.72
> LIE_41   |                             Lying is against my principles | -0.52 |       1.00 |       0.73
> LIE_29   |                             I lie whenever it’s convenient |  0.52 |       1.00 |       0.73
> LIE_27   |                        I feel tense whenever I have to lie | -0.51 |       1.00 |       0.74
> LIE_28   |                I feel satisfied when others believe my lie |  0.49 |       1.00 |       0.76
> LIE_6    |         I lie more frequently than what I expect myself to |  0.48 |       1.00 |       0.77
> LIE_25   |                                  I feel guilty after lying | -0.48 |       1.00 |       0.77
> LIE_31   |       I lie if it’s the most direct way to get what I want |  0.48 |       1.00 |       0.77
> LIE_22   |                     I find myself lying without any reason |  0.45 |       1.00 |       0.79
> LIE_23   |           I find it difficult to refrain myself from lying |  0.45 |       1.00 |       0.79
> LIE_7    |                           I lie more than I think I should |  0.45 |       1.00 |       0.80
> LIE_34   |                              I always avoid lying if I can | -0.44 |       1.00 |       0.81
> LIE_8    |                            Others lie less often than I do |  0.43 |       1.00 |       0.82
> LIE_36   | I prefer to tell the truth even if it gets me into trouble | -0.42 |       1.00 |       0.83
> LIE_44   |                                          It is bad to lie  | -0.40 |       1.00 |       0.84
> LIE_43   |                               It is okay to lie sometimes  |  0.40 |       1.00 |       0.84
> LIE_32   |            I lie when telling the truth is too troublesome |  0.38 |       1.00 |       0.85
> LIE_30   |              I lie when it’s easier than telling the truth |  0.36 |       1.00 |       0.87
> LIE_33   |                                       I lie when necessary |  0.36 |       1.00 |       0.87
> LIE_42   |           It is acceptable to lie depending on the context |  0.34 |       1.00 |       0.88
> LIE_39   |            I would lie if something important was at stake |  0.27 |       1.00 |       0.93
> LIE_3    |                                          I never tell lies | -0.26 |       1.00 |       0.93
> LIE_38   |                      I would never lie in serious contexts | -0.26 |       1.00 |       0.93
> LIE_16   |                 My lies often arouse suspicion from others | -0.25 |       1.00 |       0.94
> LIE_37   |                      I would never lie for trivial matters | -0.24 |       1.00 |       0.94
> LIE_21   |                          I have to try hard to avoid lying |  0.20 |       1.00 |       0.96
> LIE_24   |                  It is easy to hold back from telling lies | -0.13 |       1.00 |       0.98
> LIE_40   |                         I would only lie if it is harmless |  0.11 |       1.00 |       0.99
> LIE_35   |                 I would only lie if I have no other choice | -0.09 |       1.00 |       0.99
> 
> The unique latent factor (varimax rotation) accounted for 25.42% of the total variance of the original data.
> [1] "The model with one, four and seven factors accounted for 25.42, 44.18 and 48.98% of variance of the dataset."

The factor number exploration suggested the presence of seven, four and one latent factor(s). However, the seven-factors solution apppeared as spurious (one of the factor being loaded by only one item, “I never tell lies”, which is likely to be representing social desirability than genuine lying behaviour). We therefore decided to keep the unique and four-factors models and submitted their simple structure to Confirmatory Factor Analysis (CFA)

Confirmatory Factor Analysis (CFA)

Model Selection

One vs. Four Factors
Model AIC BIC BIC_adjusted Chisq RMSEA CFI SRMR
cfa_4 143607 144043 143744 3598 0.06 0.83 0.09
cfa_1 148003 148411 148131 8006 0.10 0.55 0.11
> [1] "The confirmatory factor analysis favoured the four-factors solution (X2<4-factors> = 3598.15, AIC<4-factors> = 1.43607e+05, BIC<4-factors> = 1.43744e+05, RMSEA<4-factors> = 0.06, CFI<4-factors> = 0.83, SRMR<4-factors> = 0.09) over the one-factor (X2<1-factor> = 8006.07, AIC<1-factor> = 1.48003e+05, BIC<1-factor> = 1.48131e+05, RMSEA<1-factor> = 0.10, CFI<1-factor> = 0.55, SRMR<1-factor> = 0.11)"
Four-factors vs. Initial Model
Model AIC BIC BIC_adjusted Chisq RMSEA CFI SRMR
cfa_4 143607 144043 143744 3598 0.06 0.83 0.09
cfa_initial 144979 145401 145112 4976 0.08 0.74 0.09
> [1] "We then compared the four-factors solution with the initial hypothetic model with which we built the scale, which favoured the four-factors model (X2<hypothetic> = 4976.26, AIC<hypothetic> = 1.44979e+05, BIC<hypothetic> = 1.45112e+05, RMSEA<hypothetic> = 0.08, CFI<hypothetic> = 0.74, SRMR<hypothetic> = 0.09)"
Short vs. Long Form
Model AIC BIC BIC_adjusted Chisq RMSEA CFI SRMR
cfa_4 143607 144043 143744 3598 0.06 0.83 0.09
cfa_4_short3 38449 38588 38493 115 0.04 0.98 0.04
cfa_4_short4 50693 50869 50748 321 0.05 0.96 0.05
cfa_4_short5 63472 63686 63540 623 0.06 0.94 0.06
> [1] "Finally, we compared the full four-factors model (including all items) with short form retaining only the 3, 4 or 5 most loading items for each of the 4 dimensions. The 3-items version (X2<3-items> = 115.10, AIC<3-items> = 38448.70, BIC<3-items> = 38492.52, RMSEA<3-items> = 0.04, CFI<3-items> = 0.98, SRMR<3-items> = 0.04) outperformed all versions, including 5-items (X2<5-items> = 623.33, AIC<5-items> = 63472.44, BIC<5-items> = 63539.63, RMSEA<5-items> = 0.06, CFI<5-items> = 0.94, SRMR<5-items> = 0.06) and 4-items (X2<4-items> = 320.59, AIC<4-items> = 50692.77, BIC<4-items> = 50748.27, RMSEA<4-items> = 0.05, CFI<4-items> = 0.96, SRMR<4-items> = 0.05). Nonetheless, as 3-items per construct is the bare minimum for adequate reliability, we decided to keep the second best performing version with 4-items per factor, which also displayed excellent indices of fit."

Model Description

To Operator From Coefficient SE CI_low CI_high p Type
1 Ability =~ LIE_9 1.00 0.00 1.00 1.00 0 Loading
2 Ability =~ LIE_10 1.05 0.03 0.99 1.10 0 Loading
3 Ability =~ LIE_14 0.77 0.03 0.71 0.83 0 Loading
4 Ability =~ LIE_18 0.87 0.03 0.81 0.94 0 Loading
5 Frequency =~ LIE_1 1.00 0.00 1.00 1.00 0 Loading
6 Frequency =~ LIE_4 1.01 0.04 0.93 1.09 0 Loading
7 Frequency =~ LIE_5 0.92 0.04 0.85 1.00 0 Loading
8 Frequency =~ LIE_23 0.82 0.04 0.74 0.90 0 Loading
9 Negativity =~ LIE_25 1.00 0.00 1.00 1.00 0 Loading
10 Negativity =~ LIE_34 0.86 0.06 0.73 0.99 0 Loading
11 Negativity =~ LIE_41 1.30 0.09 1.12 1.48 0 Loading
12 Negativity =~ LIE_44 1.07 0.08 0.91 1.22 0 Loading
13 Contextuality =~ LIE_33 1.00 0.00 1.00 1.00 0 Loading
14 Contextuality =~ LIE_39 0.78 0.07 0.64 0.92 0 Loading
15 Contextuality =~ LIE_42 1.03 0.07 0.90 1.16 0 Loading
16 Contextuality =~ LIE_43 1.21 0.07 1.06 1.35 0 Loading
37 Ability ~~ Frequency 2.30 0.22 1.87 2.74 0 Correlation
38 Ability ~~ Negativity -1.74 0.19 -2.12 -1.37 0 Correlation
39 Ability ~~ Contextuality 1.59 0.18 1.24 1.94 0 Correlation
40 Frequency ~~ Negativity -1.78 0.18 -2.13 -1.44 0 Correlation
41 Frequency ~~ Contextuality 0.79 0.14 0.52 1.06 0 Correlation
42 Negativity ~~ Contextuality -0.88 0.12 -1.12 -0.64 0 Correlation
data <- see::data_plot(cfa_parameters, ci=FALSE)

data$nodes <- mutate(data$nodes, Name = stringr::str_replace(Name, "LIE_", "Q")) 
data$edges <- mutate(data$edges, from = stringr::str_replace(from, "LIE_", "Q"))

figure_CFA <- tidygraph::tbl_graph(data$nodes, data$edges) %>%
  ggraph::ggraph(layout = 'fr') +
  ggraph::geom_edge_arc(aes(alpha = as.numeric(Type == "Correlation"),
                    label = Label_Correlation,
                    color = Coefficient),
                    strength = 0.1,
                    edge_width = 1.5,
                    label_dodge = unit(2, "mm"),
                    linetype = 1, angle_calc = "along",
                    label_size = 3,
                    start_cap = ggraph::circle(0, 'mm'), end_cap = ggraph::circle(0, 'mm')) +
  ggraph::geom_edge_link(aes(alpha = as.numeric(Type == "Loading"),
                     label = Label_Loading,
                     color = Coefficient),
                     label_dodge = unit(2, "mm"),
                     angle_calc = "along", 
                     edge_width = 0.9,
                     label_size = 3,
                     check_overlap = TRUE,
                     arrow = arrow(type = "closed", length = unit(3, "mm")),
                     start_cap = ggraph::circle(0, 'mm'), end_cap = ggraph::circle(-12, 'mm')) +
  ggraph::geom_node_point(aes(colour = Name, size = Latent)) +
  ggraph::geom_node_text(aes(label = Name))  +
  ggraph::scale_edge_colour_gradient2(
    guide = FALSE,
    high = "#4CAF50",
    mid = "#FFF9C4",
    low = "#E91E63"
  ) +
  scale_alpha(guide = FALSE, range = c(0, 1)) +
  scale_size_manual(values=c("TRUE"=33, "FALSE"=22)) +
  scale_color_manual(values=c("Negativity"="#E91E63", "Q41"="#EC407A", "Q44"="#F06292", "Q34"="#F48FB1", "Q25"="#F8BBD0",
                              "Contextuality"="#FF9800", "Q43"="#FFA726", "Q42"="#FFB74D", "Q33"="#FFCC80", "Q39"="#FFE0B2",
                              "Frequency"="#4CAF50", "Q1"="#66BB6A", "Q4"="#81C784", "Q5"="#A5D6A7", "Q23"="#C8E6C9",
                              "Ability"="#2196F3", "Q10"="#42A5F5", "Q9"="#64B5F6", "Q18"="#90CAF9", "Q14"="#BBDEFB")) +
  ggraph::scale_edge_alpha(guide = FALSE, range = c(0, 1)) +
  scale_x_continuous(expand = expand_scale(c(0.07, 0.07))) +
  scale_y_continuous(expand = expand_scale(c(0.07, 0.07))) +
  ggraph::theme_graph() +
  theme(legend.position = "none")

Reliability

Cronbach’s alpha

> [1] "All subscales of the LIE scale, namely, Frequency (alpha = 0.86), Ability (alpha = 0.90), Contextuality (alpha = 0.75) and Negativity (alpha = 0.75) have a high reliability."
Omega (total) Omega (hierarchical) Omega (group)
All items 0.83 0.36 0.50
Ability 0.91 0.44 0.47
Frequency 0.87 0.37 0.50
Contextuality 0.76 0.19 0.57
Negativity 0.75 0.37 0.38
Composite Total Variance (%) Variance due to General Factor (%) Variance due to Group Factor (%)
All items 83 36 50
Ability 91 44 47
Frequency 87 37 50
Contextuality 76 19 57
Negativity 75 37 38

Cluster Structure

Cluster Tendency

> # Clustering tendency
> 
> The dataset is suitable for clustering (Hopkins' H = 0.24).

How many clusters

> # Method Agreement Procedure:
> 
> The choice of 3 clusters is supported by 11 (39.29%) methods out of 28 (KL, Hartigan, Scott, Marriot, TrCovW, TraceW, Friedman, Rubin, Ball, PtBiserial, Mixture).

The agreement procedure, combining 33 different methods for determining the optimal number of clusters, supported the existence of 2 (12/33) or 3 (11/33) clusters.

Clustering

K-means

Cluster n_Obs Sum_Squares LIE_Ability LIE_Frequency LIE_Negativity LIE_Contextuality
1 310 2256 -2.1 -1.42 0.97 -0.74
2 452 2947 1.5 0.97 -0.67 0.50
Cluster n_Obs Sum_Squares LIE_Ability LIE_Frequency LIE_Negativity LIE_Contextuality
1 319 1711 0.19 -0.53 0.15 0.11
2 267 1317 1.92 1.85 -1.09 0.61
3 176 979 -3.25 -1.85 1.38 -1.13
> [1] "We applied k-means clustering, which revealed that grouping the participants in 2 and 3 clusters would account for 44.92% and 57.58% of the total variance of the four dimensions of the questionnaire, respectively. Thus, we decided to go ahead with the latter solution."

Compute Profiles

> [1] "We then assigned each participant to its nearest cluster, labelling them as Average (41.86% of the sample; people that report an average lying ability, slightly lower than average frequency, average negativity and contextuality), Trickster (35.04%; people with high reported lying ability, frequency, low negative experience associated with deception and above-average flexibility in its implementation), and Virtuous (23.10%; people with very low reported lying ability and frequency, strong negative emotions and moral attitude associated with lying and high rigidity in their (non-)usage of deception)."

Convergent Validity

Demographics

Sex

Model Type ELPD ELPD_SE LOOIC LOOIC_SE R2
model_dimensional stanreg -516 5.8 1032 12 0.03
model_profile stanreg -516 5.5 1031 11 0.03
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
(Intercept) -0.29 [-0.46, -0.10] 99.25% 16.95% 1.000 3295 Normal (0 +- 10.00)
LIE_Profile [Trickster] 0.42 [ 0.16, 0.68] 99.52% 7.92% 1.001 3348 Normal (0 +- 2.50)
LIE_Profile [Virtuous] -0.48 [-0.79, -0.16] 99.35% 6.55% 1.000 3126 Normal (0 +- 2.50)
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
1 (Intercept) -0.26 [-0.36, -0.13] 100% 15.80% 0.999 3165 Normal (0 +- 10.00)
2 LIE_Ability 0.14 [ 0.07, 0.20] 100% 85.85% 1.000 3615 Normal (0 +- 1.08)
4 LIE_Frequency 0.02 [-0.07, 0.11] 62.32% 99.85% 1.002 2934 Normal (0 +- 1.34)
3 LIE_Contextuality -0.04 [-0.16, 0.06] 72.50% 97.85% 1.001 3124 Normal (0 +- 1.87)
5 LIE_Negativity -0.06 [-0.18, 0.09] 75.25% 92.25% 1.002 2818 Normal (0 +- 1.89)

sig <- model_parameters(model_dimensional)[-1,] %>% 
  select(Parameter, pd) %>% 
  mutate(Dimension = stringr::str_remove(Parameter, "LIE_"),
         Text = format_pd(pd, stars_only=TRUE),
         Median = 0.6,
         Score = df %>% 
           select(one_of(Parameter)) %>% 
           summarise_all(function(x) {mean(range(x))}) %>% 
           t()) %>% 
  mutate(Dimension = fct_relevel(Dimension, "Ability", "Frequency", "Negativity", "Contextuality"))

p_sex <- rbind(estimate_link(model_dimensional, target="LIE_Ability") %>% 
        mutate(LIE_Frequency = NA, LIE_Contextuality=NA, LIE_Negativity=NA),
      estimate_link(model_dimensional, target="LIE_Frequency") %>% 
        mutate(LIE_Ability = NA, LIE_Contextuality=NA, LIE_Negativity=NA),
      estimate_link(model_dimensional, target="LIE_Contextuality") %>% 
        mutate(LIE_Frequency = NA, LIE_Ability=NA, LIE_Negativity=NA),
      estimate_link(model_dimensional, target="LIE_Negativity") %>% 
        mutate(LIE_Frequency = NA, LIE_Contextuality=NA, LIE_Ability=NA)) %>% 
  pivot_longer(cols=starts_with("LIE_"), names_to="Dimension", values_to = "Score") %>% 
  mutate(Dimension = str_remove(Dimension, "LIE_"),
         Dimension = fct_relevel(Dimension, "Ability", "Frequency", "Negativity", "Contextuality")) %>% 
  ggplot(aes(x = Score, y = Median)) +
  geom_ribbon(aes(ymin=CI_low, ymax=CI_high, fill=Dimension), alpha=0.1) +
  geom_line(aes(color=Dimension), size = 1) +
  geom_text(data = sig, aes(label = Text)) +
  theme_modern() +
  theme(strip.placement = "outside",
        strip.text = element_text(size=13, face="plain"),
        axis.title = element_text(size=13),
        axis.text = element_text(size=9),
        plot.title = element_text(face="bold", hjust = 0.5)) +
  ggtitle("Sex") +
  ylab("Probability of being a Male") +
  xlab("") +
  scale_color_manual(values=c("Ability"= "#2196F3", "Frequency"="#4CAF50", "Negativity"="#E91E63", "Contextuality"="#FF9800"), name = "Dimensions", guide=FALSE) +
   scale_fill_manual(values=c("Ability"= "#2196F3", "Frequency"="#4CAF50", "Negativity"="#E91E63", "Contextuality"="#FF9800"), name = "Dimensions", guide=FALSE) +
  facet_wrap(~Dimension, scales="free_x", strip.position = "bottom")

Age

Model Type ELPD ELPD_SE LOOIC LOOIC_SE WAIC R2 R2_marginal R2_adjusted RMSE
model_dimensional stanreg -2194 37 4388 75 4388 0.26 0.26 0.23 6.7
model_profile stanreg -2191 37 4382 73 4382 0.26 0.26 0.24 6.6
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
1 (Intercept) 31.25 [24.47, 33.43] 100% 0% 1.892 7 Normal (0 +- 77.55)
4 LIE_Profile [Trickster] -0.46 [-1.33, 0.49] 79.65% 73.62% 1.005 1949 Normal (0 +- 19.39)
5 LIE_Profile [Virtuous] 2.83 [ 1.75, 3.82] 100% 0.12% 1.003 946 Normal (0 +- 19.39)
3 Income 0.00 [ 0.00, 0.00] 99.78% 100% 1.249 18 Normal (0 +- 0.00)
2 Education -1.96 [-2.17, -1.76] 100% 0% 1.000 2214 Normal (0 +- 9.71)
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
1 (Intercept) 31.80 [29.53, 34.18] 100% 0% 1.033 112 Normal (0 +- 77.55)
4 LIE_Ability -0.35 [-0.58, -0.11] 99.08% 99.85% 1.002 1419 Normal (0 +- 8.39)
6 LIE_Frequency 0.02 [-0.30, 0.34] 53.65% 100% 1.000 2339 Normal (0 +- 10.26)
5 LIE_Contextuality -0.26 [-0.63, 0.12] 85.90% 98.72% 1.000 2926 Normal (0 +- 14.46)
7 LIE_Negativity 0.19 [-0.30, 0.67] 74.92% 96.62% 0.999 2529 Normal (0 +- 14.51)
3 Income 0.00 [ 0.00, 0.00] 99.83% 100% 1.002 3757 Normal (0 +- 0.00)
2 Education -1.93 [-2.13, -1.71] 100% 0% 1.006 849 Normal (0 +- 9.71)

sig <- model_parameters(model_dimensional)[2:5,] %>% 
  select(Parameter, pd) %>% 
  mutate(Dimension = stringr::str_remove(Parameter, "LIE_"),
         Text = format_pd(pd, stars_only=TRUE),
         Median = 29,
         Score = df %>% 
           select(one_of(Parameter)) %>% 
           summarise_all(function(x) {mean(range(x))}) %>% 
           t()) %>% 
  mutate(Dimension = fct_relevel(Dimension, "Ability", "Frequency", "Negativity", "Contextuality"))

p_age <- rbind(estimate_link(model_dimensional, target="LIE_Ability") %>% 
        mutate(LIE_Frequency = NA, LIE_Contextuality=NA, LIE_Negativity=NA),
      estimate_link(model_dimensional, target="LIE_Frequency") %>% 
        mutate(LIE_Ability = NA, LIE_Contextuality=NA, LIE_Negativity=NA),
      estimate_link(model_dimensional, target="LIE_Contextuality") %>% 
        mutate(LIE_Frequency = NA, LIE_Ability=NA, LIE_Negativity=NA),
      estimate_link(model_dimensional, target="LIE_Negativity") %>% 
        mutate(LIE_Frequency = NA, LIE_Contextuality=NA, LIE_Ability=NA)) %>% 
  pivot_longer(cols=starts_with("LIE_"), names_to="Dimension", values_to = "Score") %>% 
  mutate(Dimension = str_remove(Dimension, "LIE_"),
         Dimension = fct_relevel(Dimension, "Ability", "Frequency", "Negativity", "Contextuality")) %>% 
  ggplot(aes(x = Score, y = Median)) +
  geom_ribbon(aes(ymin=CI_low, ymax=CI_high, fill=Dimension), alpha=0.1) +
  geom_line(aes(color=Dimension), size = 1) +
  geom_text(data = sig, aes(label = Text)) +
  theme_modern() +
  theme(strip.placement = "outside",
        strip.text = element_text(size=13, face="plain"),
        axis.title = element_text(size=13),
        axis.text = element_text(size=9),
        plot.title = element_text(face="bold", hjust = 0.5)) +
  ggtitle("Age") +
  ylab("\nAge") +
  xlab("") +
  scale_color_manual(values=c("Ability"= "#2196F3", "Frequency"="#4CAF50", "Negativity"="#E91E63", "Contextuality"="#FF9800"), name = "Dimensions", guide=FALSE) +
   scale_fill_manual(values=c("Ability"= "#2196F3", "Frequency"="#4CAF50", "Negativity"="#E91E63", "Contextuality"="#FF9800"), name = "Dimensions", guide=FALSE) +
  facet_wrap(~Dimension, scales="free_x", strip.position = "bottom")

Education

Model Type ELPD ELPD_SE LOOIC LOOIC_SE WAIC R2 R2_marginal R2_adjusted RMSE
model_dimensional stanreg -1502 33 3003 66 3003 0.25 0.25 0.23 1.7
model_profile stanreg -1499 33 2998 66 2998 0.25 0.25 0.23 1.7
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
1 (Intercept) 6.73 [ 6.05, 7.44] 100% 0% 1.008 687 Normal (0 +- 20.05)
3 LIE_Profile [Trickster] -0.35 [-0.57, -0.10] 98.88% 16.18% 1.001 2457 Normal (0 +- 5.01)
4 LIE_Profile [Virtuous] 0.38 [ 0.10, 0.64] 98.65% 14.05% 1.000 2141 Normal (0 +- 5.01)
2 Age -0.13 [-0.14, -0.11] 100% 100% 1.000 3465 Normal (0 +- 0.65)
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
1 (Intercept) 6.67 [ 6.02, 7.29] 100% 0% 1.016 287 Normal (0 +- 20.05)
3 LIE_Ability -0.06 [-0.12, -0.01] 95.20% 100% 1.002 2008 Normal (0 +- 2.16)
5 LIE_Frequency -0.09 [-0.18, -0.01] 95.95% 98.55% 1.006 528 Normal (0 +- 2.69)
4 LIE_Contextuality 0.04 [-0.05, 0.14] 78.50% 99.75% 1.002 2307 Normal (0 +- 3.75)
6 LIE_Negativity 0.03 [-0.13, 0.13] 61.72% 98.88% 1.005 685 Normal (0 +- 3.81)
2 Age -0.12 [-0.14, -0.11] 100% 100% 1.000 3471 Normal (0 +- 0.65)

Income

Model Type ELPD ELPD_SE LOOIC LOOIC_SE WAIC R2 R2_marginal R2_adjusted RMSE
model_dimensional stanreg -6464 139 12928 277 12939 0.04 0.04 0.02 4276
model_profile stanreg -6465 140 12931 279 12948 0.04 0.04 0.02 4283
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
1 (Intercept) -694.65 [-2637.02, 1063.63] 74.30% 25.42% 1.012 502 Normal (0 +- 43581.74)
4 LIE_Profile [Trickster] 438.04 [ -219.35, 1007.82] 86.58% 48.52% 1.001 2535 Normal (0 +- 10895.43)
5 LIE_Profile [Virtuous] -398.41 [-1073.16, 402.18] 81.10% 50.40% 1.000 2254 Normal (0 +- 10895.43)
2 Age 75.17 [ 35.98, 116.60] 99.88% 100% 1.000 2519 Normal (0 +- 1404.88)
3 Education 436.75 [ 278.79, 588.01] 100% 49.55% 1.001 2247 Normal (0 +- 5456.14)
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
1 (Intercept) -671.17 [-2634.41, 1401.03] 73.55% 26.35% 1.040 96 Normal (0 +- 43581.74)
4 LIE_Ability 184.52 [ 14.68, 318.99] 97.45% 99.55% 1.004 1487 Normal (0 +- 4714.92)
6 LIE_Frequency 43.43 [ -187.70, 231.38] 62.70% 99.90% 1.004 2524 Normal (0 +- 5763.34)
5 LIE_Contextuality -151.32 [ -383.15, 109.20] 81.75% 96.53% 1.010 554 Normal (0 +- 8123.93)
7 LIE_Negativity 9.49 [ -289.18, 352.67] 51.90% 97.10% 1.002 2927 Normal (0 +- 8156.44)
2 Age 76.07 [ 37.06, 113.42] 99.88% 100% 1.003 1827 Normal (0 +- 1404.88)
3 Education 438.42 [ 283.65, 587.92] 100% 48.93% 1.004 2073 Normal (0 +- 5456.14)

sig <- model_parameters(model_dimensional)[2:5,] %>% 
  select(Parameter, pd) %>% 
  mutate(Dimension = stringr::str_remove(Parameter, "LIE_"),
         Text = format_pd(pd, stars_only=TRUE),
         Median = 4500,
         Score = df %>% 
           select(one_of(Parameter)) %>% 
           summarise_all(function(x) {mean(range(x))}) %>% 
           t()) %>% 
  mutate(Dimension = fct_relevel(Dimension, "Ability", "Frequency", "Negativity", "Contextuality"))

p_income <- rbind(estimate_link(model_dimensional, target="LIE_Ability") %>% 
        mutate(LIE_Frequency = NA, LIE_Contextuality=NA, LIE_Negativity=NA),
      estimate_link(model_dimensional, target="LIE_Frequency") %>% 
        mutate(LIE_Ability = NA, LIE_Contextuality=NA, LIE_Negativity=NA),
      estimate_link(model_dimensional, target="LIE_Contextuality") %>% 
        mutate(LIE_Frequency = NA, LIE_Ability=NA, LIE_Negativity=NA),
      estimate_link(model_dimensional, target="LIE_Negativity") %>% 
        mutate(LIE_Frequency = NA, LIE_Contextuality=NA, LIE_Ability=NA)) %>% 
  pivot_longer(cols=starts_with("LIE_"), names_to="Dimension", values_to = "Score") %>% 
  mutate(Dimension = str_remove(Dimension, "LIE_"),
         Dimension = fct_relevel(Dimension, "Ability", "Frequency", "Negativity", "Contextuality")) %>% 
  ggplot(aes(x = Score, y = Median)) +
  geom_ribbon(aes(ymin=CI_low, ymax=CI_high, fill=Dimension), alpha=0.1) +
  geom_line(aes(color=Dimension), size = 1) +
  geom_text(data = sig, aes(label = Text)) +
  theme_modern() +
  theme(strip.placement = "outside",
        strip.text = element_text(size=13, face="plain"),
        axis.title = element_text(size=13),
        axis.text = element_text(size=9),
        plot.title = element_text(face="bold", hjust = 0.5)) +
  ggtitle("Income") +
  ylab("Income (in SGD per capita)") +
  xlab("") +
  scale_color_manual(values=c("Ability"= "#2196F3", "Frequency"="#4CAF50", "Negativity"="#E91E63", "Contextuality"="#FF9800"), name = "Dimensions", guide=FALSE) +
   scale_fill_manual(values=c("Ability"= "#2196F3", "Frequency"="#4CAF50", "Negativity"="#E91E63", "Contextuality"="#FF9800"), name = "Dimensions", guide=FALSE) +
  facet_wrap(~Dimension, scales="free_x", strip.position = "bottom")

Religion

Model Type ELPD ELPD_SE LOOIC LOOIC_SE WAIC R2 R2_marginal R2_adjusted RMSE
model_dimensional stanreg -1621 17 3242 34 3242 0.41 0.10 0.40 2.2
model_profile stanreg -1641 17 3282 33 3282 0.38 0.03 0.37 2.3
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
(Intercept) 4.37 [ 2.91, 5.68] 100% 0% 1.006 878 Normal (0 +- 29.40)
LIE_Profile [Trickster] -0.37 [-0.67, -0.02] 96.95% 35.43% 1.001 3454 Normal (0 +- 7.35)
LIE_Profile [Virtuous] 0.66 [ 0.28, 1.01] 99.85% 5.33% 1.002 3039 Normal (0 +- 7.35)
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
1 (Intercept) 4.43 [ 3.32, 5.69] 100% 0% 1.004 845 Normal (0 +- 29.40)
2 LIE_Ability -0.01 [-0.08, 0.07] 59.62% 100% 1.000 3751 Normal (0 +- 3.19)
4 LIE_Frequency 0.25 [ 0.15, 0.35] 100% 75.98% 1.000 3285 Normal (0 +- 3.96)
3 LIE_Contextuality -0.25 [-0.36, -0.12] 99.92% 71.28% 1.000 4248 Normal (0 +- 5.47)
5 LIE_Negativity 0.53 [ 0.37, 0.69] 100% 0.85% 0.999 3538 Normal (0 +- 5.57)

sig <- model_parameters(model_dimensional)[-1,] %>% 
  select(Parameter, pd) %>% 
  mutate(Dimension = stringr::str_remove(Parameter, "LIE_"),
         Text = format_pd(pd, stars_only=TRUE),
         Median = 6.5,
         Score = dplyr::filter(df, !is.na(Religion_Faith)) %>% 
           select(one_of(Parameter)) %>% 
           summarise_all(function(x) {mean(range(x))}) %>% 
           t()) %>% 
  mutate(Dimension = fct_relevel(Dimension, "Ability", "Frequency", "Negativity", "Contextuality"))


p_religion <- rbind(estimate_link(model_dimensional, target="LIE_Ability") %>% 
        mutate(LIE_Frequency = NA, LIE_Contextuality=NA, LIE_Negativity=NA),
      estimate_link(model_dimensional, target="LIE_Frequency") %>% 
        mutate(LIE_Ability = NA, LIE_Contextuality=NA, LIE_Negativity=NA),
      estimate_link(model_dimensional, target="LIE_Contextuality") %>% 
        mutate(LIE_Frequency = NA, LIE_Ability=NA, LIE_Negativity=NA),
      estimate_link(model_dimensional, target="LIE_Negativity") %>% 
        mutate(LIE_Frequency = NA, LIE_Contextuality=NA, LIE_Ability=NA)) %>% 
  pivot_longer(cols=starts_with("LIE_"), names_to="Dimension", values_to = "Score") %>% 
  mutate(Dimension = str_remove(Dimension, "LIE_"),
         Dimension = fct_relevel(Dimension, "Ability", "Frequency", "Negativity", "Contextuality")) %>% 
  ggplot(aes(x = Score, y = Median)) +
  geom_ribbon(aes(ymin=CI_low, ymax=CI_high, fill=Dimension), alpha=0.1) +
  geom_line(aes(color=Dimension), size = 1) +
  theme_modern() +
  theme(strip.placement = "outside",
        strip.text = element_text(size=13, face="plain"),
        axis.title = element_text(size=13),
        axis.text = element_text(size=9),
        plot.title = element_text(face="bold", hjust = 0.5)) +
  geom_text(data = sig, aes(label = Text)) +
  ggtitle("Religion") +
  ylab("\nFaith") +
  xlab("") +
  scale_color_manual(values=c("Ability"= "#2196F3", "Frequency"="#4CAF50", "Negativity"="#E91E63", "Contextuality"="#FF9800"), name = "Dimensions", guide=FALSE) +
   scale_fill_manual(values=c("Ability"= "#2196F3", "Frequency"="#4CAF50", "Negativity"="#E91E63", "Contextuality"="#FF9800"), name = "Dimensions", guide=FALSE) +
  facet_wrap(~Dimension, scales="free_x", strip.position = "bottom")

Number of Lies

Raw

Model Type ELPD ELPD_SE LOOIC LOOIC_SE WAIC R2 R2_adjusted RMSE
model_dimensional stanreg -903 22 1807 44 1807 0.16 0.15 0.87
model_profile stanreg -918 21 1837 42 1837 0.12 0.11 0.89
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
(Intercept) 1.08 [ 1.00, 1.16] 100% 0% 1.000 3811 Normal (0 +- 9.45)
LIE_Profile [Trickster] 0.51 [ 0.38, 0.63] 100% 0% 1.001 3963 Normal (0 +- 2.36)
LIE_Profile [Virtuous] -0.35 [-0.48, -0.22] 100% 0.25% 1.000 3701 Normal (0 +- 2.36)
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
1 (Intercept) 1.18 [ 1.13, 1.23] 100% 0% 1.000 3520 Normal (0 +- 9.45)
2 LIE_Ability 0.01 [-0.02, 0.04] 65.22% 100% 1.000 3929 Normal (0 +- 1.02)
4 LIE_Frequency 0.17 [ 0.13, 0.21] 100% 0.10% 1.002 3074 Normal (0 +- 1.31)
3 LIE_Contextuality 0.05 [ 0.00, 0.09] 94.33% 95.40% 1.000 3171 Normal (0 +- 1.76)
5 LIE_Negativity -0.03 [-0.09, 0.03] 77.15% 95.10% 1.001 2993 Normal (0 +- 1.83)

Adjusted

Model Type ELPD ELPD_SE LOOIC LOOIC_SE WAIC R2 R2_adjusted RMSE
model_dimensional stanreg -913 22 1825 44 1825 0.10 0.08 0.88
model_profile stanreg -919 21 1839 43 1839 0.07 0.07 0.89
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
(Intercept) 1.46 [ 1.39, 1.55] 100% 0% 1.000 4229 Normal (0 +- 9.23)
LIE_Profile [Trickster] 0.41 [ 0.29, 0.54] 100% 0% 1.000 3645 Normal (0 +- 2.31)
LIE_Profile [Virtuous] -0.24 [-0.37, -0.11] 99.92% 3.67% 1.000 4240 Normal (0 +- 2.31)
Parameter Median 89% CI pd % in ROPE Rhat ESS Prior
1 (Intercept) 1.56 [ 1.50, 1.61] 100% 0% 1.000 4006 Normal (0 +- 9.23)
2 LIE_Ability 0.01 [-0.02, 0.04] 76.08% 100% 0.999 3458 Normal (0 +- 1.00)
4 LIE_Frequency 0.14 [ 0.10, 0.19] 100% 2.93% 1.000 3260 Normal (0 +- 1.28)
3 LIE_Contextuality 0.01 [-0.04, 0.06] 62.45% 99.75% 1.001 3456 Normal (0 +- 1.72)
5 LIE_Negativity -0.01 [-0.08, 0.05] 58.83% 97.12% 1.000 3219 Normal (0 +- 1.79)

Social Desirability (BIDR)

Utility Functions

Gaussian Graphical Model

Parameter1 Parameter2 r t df p 95% CI Method n_Obs
LIE_Ability LIE_Frequency 0.27 7.78 760 < .001 [ 0.20, 0.34] Pearson 762
LIE_Ability LIE_Negativity -0.17 -4.64 760 < .001 [-0.23, -0.10] Pearson 762
LIE_Ability LIE_Contextuality 0.34 9.92 760 < .001 [ 0.27, 0.40] Pearson 762
LIE_Ability BIDR_SelfDeceptiveEnhancement 0.21 5.98 760 < .001 [ 0.14, 0.28] Pearson 762
LIE_Ability BIDR_ImpressionManagement -0.08 -2.21 760 0.027 [-0.15, -0.01] Pearson 762
LIE_Frequency LIE_Negativity -0.55 -18.09 760 < .001 [-0.60, -0.50] Pearson 762
LIE_Frequency LIE_Contextuality -0.17 -4.63 760 < .001 [-0.23, -0.10] Pearson 762
LIE_Frequency BIDR_SelfDeceptiveEnhancement -0.20 -5.62 760 < .001 [-0.27, -0.13] Pearson 762
LIE_Frequency BIDR_ImpressionManagement -0.13 -3.51 760 < .001 [-0.20, -0.06] Pearson 762
LIE_Negativity LIE_Contextuality -0.26 -7.54 760 < .001 [-0.33, -0.20] Pearson 762
LIE_Negativity BIDR_SelfDeceptiveEnhancement -0.07 -1.90 760 0.057 [-0.14, 0.00] Pearson 762
LIE_Negativity BIDR_ImpressionManagement 0.12 3.35 760 < .001 [ 0.05, 0.19] Pearson 762
LIE_Contextuality BIDR_SelfDeceptiveEnhancement -0.11 -2.95 760 0.003 [-0.18, -0.04] Pearson 762
LIE_Contextuality BIDR_ImpressionManagement -0.17 -4.80 760 < .001 [-0.24, -0.10] Pearson 762
BIDR_SelfDeceptiveEnhancement BIDR_ImpressionManagement 0.26 7.50 760 < .001 [ 0.20, 0.33] Pearson 762

After controlling for social desirability, it seems that the relationship between frequency and contextuality changed from being positive to negative. The more people lie (likely representing a lack of control), and the less flexible and insensitive to the context they are.

Psychopathy (TRIMP)

Parameter1 Parameter2 r t df p 95% CI Method n_Obs
LIE_Ability LIE_Frequency 0.26 7.44 755 < .001 [ 0.19, 0.33] Pearson 757
LIE_Ability LIE_Negativity -0.15 -4.06 755 < .001 [-0.22, -0.08] Pearson 757
LIE_Ability LIE_Contextuality 0.34 10.09 755 < .001 [ 0.28, 0.41] Pearson 757
LIE_Ability TRIMP_Boldness 0.23 6.50 755 < .001 [ 0.16, 0.30] Pearson 757
LIE_Ability TRIMP_Meanness -0.02 -0.53 755 0.596 [-0.09, 0.05] Pearson 757
LIE_Ability TRIMP_Disinhibition 0.04 0.99 755 0.325 [-0.04, 0.11] Pearson 757
LIE_Frequency LIE_Negativity -0.53 -17.25 755 < .001 [-0.58, -0.48] Pearson 757
LIE_Frequency LIE_Contextuality -0.15 -4.17 755 < .001 [-0.22, -0.08] Pearson 757
LIE_Frequency TRIMP_Boldness 0.00 0.01 755 0.991 [-0.07, 0.07] Pearson 757
LIE_Frequency TRIMP_Meanness -0.03 -0.71 755 0.476 [-0.10, 0.05] Pearson 757
LIE_Frequency TRIMP_Disinhibition 0.23 6.62 755 < .001 [ 0.17, 0.30] Pearson 757
LIE_Negativity LIE_Contextuality -0.26 -7.53 755 < .001 [-0.33, -0.20] Pearson 757
LIE_Negativity TRIMP_Boldness 0.03 0.74 755 0.461 [-0.04, 0.10] Pearson 757
LIE_Negativity TRIMP_Meanness -0.19 -5.31 755 < .001 [-0.26, -0.12] Pearson 757
LIE_Negativity TRIMP_Disinhibition 0.15 4.09 755 < .001 [ 0.08, 0.22] Pearson 757
LIE_Contextuality TRIMP_Boldness -0.08 -2.25 755 0.024 [-0.15, -0.01] Pearson 757
LIE_Contextuality TRIMP_Meanness -0.03 -0.79 755 0.431 [-0.10, 0.04] Pearson 757
LIE_Contextuality TRIMP_Disinhibition -0.01 -0.36 755 0.719 [-0.08, 0.06] Pearson 757
TRIMP_Boldness TRIMP_Meanness 0.29 8.18 755 < .001 [ 0.22, 0.35] Pearson 757
TRIMP_Boldness TRIMP_Disinhibition -0.30 -8.60 755 < .001 [-0.36, -0.23] Pearson 757
TRIMP_Meanness TRIMP_Disinhibition 0.62 21.84 755 < .001 [ 0.58, 0.66] Pearson 757

Narcissism (FFNI)

Parameter1 Parameter2 r t df p 95% CI Method n_Obs
LIE_Ability LIE_Frequency 0.24 6.80 755 < .001 [ 0.17, 0.31] Pearson 757
LIE_Ability LIE_Negativity -0.17 -4.81 755 < .001 [-0.24, -0.10] Pearson 757
LIE_Ability LIE_Contextuality 0.33 9.66 755 < .001 [ 0.27, 0.39] Pearson 757
LIE_Ability FFNI_AcclaimSeeking -0.03 -0.74 755 0.459 [-0.10, 0.04] Pearson 757
LIE_Ability FFNI_Distrust 0.06 1.62 755 0.105 [-0.01, 0.13] Pearson 757
LIE_Ability FFNI_Entitlement -0.02 -0.63 755 0.527 [-0.09, 0.05] Pearson 757
LIE_Ability FFNI_Exploitativeness -0.12 -3.22 755 0.001 [-0.19, -0.05] Pearson 757
LIE_Ability FFNI_Indifference -0.01 -0.39 755 0.696 [-0.09, 0.06] Pearson 757
LIE_Ability FFNI_LackOfEmpathy -0.05 -1.29 755 0.198 [-0.12, 0.02] Pearson 757
LIE_Ability FFNI_Manipulativeness 0.35 10.42 755 < .001 [ 0.29, 0.42] Pearson 757
LIE_Ability FFNI_NeedForAdmiration 0.00 0.05 755 0.963 [-0.07, 0.07] Pearson 757
LIE_Ability FFNI_ThrillSeeking 0.03 0.77 755 0.442 [-0.04, 0.10] Pearson 757
LIE_Frequency LIE_Negativity -0.54 -17.68 755 < .001 [-0.59, -0.49] Pearson 757
LIE_Frequency LIE_Contextuality -0.17 -4.72 755 < .001 [-0.24, -0.10] Pearson 757
LIE_Frequency FFNI_AcclaimSeeking 0.02 0.46 755 0.649 [-0.05, 0.09] Pearson 757
LIE_Frequency FFNI_Distrust -0.03 -0.78 755 0.436 [-0.10, 0.04] Pearson 757
LIE_Frequency FFNI_Entitlement 0.08 2.33 755 0.020 [ 0.01, 0.15] Pearson 757
LIE_Frequency FFNI_Exploitativeness -0.01 -0.30 755 0.761 [-0.08, 0.06] Pearson 757
LIE_Frequency FFNI_Indifference 0.06 1.67 755 0.096 [-0.01, 0.13] Pearson 757
LIE_Frequency FFNI_LackOfEmpathy 0.10 2.84 755 0.005 [ 0.03, 0.17] Pearson 757
LIE_Frequency FFNI_Manipulativeness -0.01 -0.25 755 0.802 [-0.08, 0.06] Pearson 757
LIE_Frequency FFNI_NeedForAdmiration 0.10 2.69 755 0.007 [ 0.03, 0.17] Pearson 757
LIE_Frequency FFNI_ThrillSeeking 0.04 1.01 755 0.312 [-0.03, 0.11] Pearson 757
LIE_Negativity LIE_Contextuality -0.26 -7.53 755 < .001 [-0.33, -0.20] Pearson 757
LIE_Negativity FFNI_AcclaimSeeking 0.10 2.81 755 0.005 [ 0.03, 0.17] Pearson 757
LIE_Negativity FFNI_Distrust -0.01 -0.24 755 0.811 [-0.08, 0.06] Pearson 757
LIE_Negativity FFNI_Entitlement 0.09 2.52 755 0.012 [ 0.02, 0.16] Pearson 757
LIE_Negativity FFNI_Exploitativeness -0.13 -3.48 755 < .001 [-0.20, -0.05] Pearson 757
LIE_Negativity FFNI_Indifference 0.04 1.12 755 0.261 [-0.03, 0.11] Pearson 757
LIE_Negativity FFNI_LackOfEmpathy 0.00 -0.07 755 0.943 [-0.07, 0.07] Pearson 757
LIE_Negativity FFNI_Manipulativeness 0.06 1.73 755 0.083 [-0.01, 0.13] Pearson 757
LIE_Negativity FFNI_NeedForAdmiration 0.10 2.88 755 0.004 [ 0.03, 0.17] Pearson 757
LIE_Negativity FFNI_ThrillSeeking -0.02 -0.49 755 0.627 [-0.09, 0.05] Pearson 757
LIE_Contextuality FFNI_AcclaimSeeking 0.11 2.98 755 0.003 [ 0.04, 0.18] Pearson 757
LIE_Contextuality FFNI_Distrust 0.02 0.55 755 0.581 [-0.05, 0.09] Pearson 757
LIE_Contextuality FFNI_Entitlement -0.04 -0.97 755 0.330 [-0.11, 0.04] Pearson 757
LIE_Contextuality FFNI_Exploitativeness 0.04 0.98 755 0.327 [-0.04, 0.11] Pearson 757
LIE_Contextuality FFNI_Indifference 0.04 1.22 755 0.224 [-0.03, 0.12] Pearson 757
LIE_Contextuality FFNI_LackOfEmpathy 0.01 0.27 755 0.784 [-0.06, 0.08] Pearson 757
LIE_Contextuality FFNI_Manipulativeness -0.11 -2.91 755 0.004 [-0.18, -0.03] Pearson 757
LIE_Contextuality FFNI_NeedForAdmiration 0.09 2.47 755 0.014 [ 0.02, 0.16] Pearson 757
LIE_Contextuality FFNI_ThrillSeeking 0.03 0.72 755 0.473 [-0.05, 0.10] Pearson 757
FFNI_AcclaimSeeking FFNI_Distrust 0.05 1.36 755 0.173 [-0.02, 0.12] Pearson 757
FFNI_AcclaimSeeking FFNI_Entitlement 0.12 3.38 755 < .001 [ 0.05, 0.19] Pearson 757
FFNI_AcclaimSeeking FFNI_Exploitativeness 0.01 0.14 755 0.885 [-0.07, 0.08] Pearson 757
FFNI_AcclaimSeeking FFNI_Indifference 0.05 1.49 755 0.137 [-0.02, 0.12] Pearson 757
FFNI_AcclaimSeeking FFNI_LackOfEmpathy -0.15 -4.30 755 < .001 [-0.22, -0.08] Pearson 757
FFNI_AcclaimSeeking FFNI_Manipulativeness 0.20 5.73 755 < .001 [ 0.13, 0.27] Pearson 757
FFNI_AcclaimSeeking FFNI_NeedForAdmiration -0.06 -1.62 755 0.106 [-0.13, 0.01] Pearson 757
FFNI_AcclaimSeeking FFNI_ThrillSeeking 0.17 4.87 755 < .001 [ 0.10, 0.24] Pearson 757
FFNI_Distrust FFNI_Entitlement 0.08 2.08 755 0.038 [ 0.00, 0.15] Pearson 757
FFNI_Distrust FFNI_Exploitativeness 0.18 4.95 755 < .001 [ 0.11, 0.25] Pearson 757
FFNI_Distrust FFNI_Indifference 0.11 3.12 755 0.002 [ 0.04, 0.18] Pearson 757
FFNI_Distrust FFNI_LackOfEmpathy 0.06 1.78 755 0.076 [-0.01, 0.14] Pearson 757
FFNI_Distrust FFNI_Manipulativeness -0.06 -1.52 755 0.129 [-0.13, 0.02] Pearson 757
FFNI_Distrust FFNI_NeedForAdmiration 0.17 4.83 755 < .001 [ 0.10, 0.24] Pearson 757
FFNI_Distrust FFNI_ThrillSeeking -0.04 -1.06 755 0.288 [-0.11, 0.03] Pearson 757
FFNI_Entitlement FFNI_Exploitativeness 0.28 8.01 755 < .001 [ 0.21, 0.34] Pearson 757
FFNI_Entitlement FFNI_Indifference -0.03 -0.73 755 0.467 [-0.10, 0.04] Pearson 757
FFNI_Entitlement FFNI_LackOfEmpathy 0.24 6.82 755 < .001 [ 0.17, 0.31] Pearson 757
FFNI_Entitlement FFNI_Manipulativeness 0.07 1.95 755 0.052 [ 0.00, 0.14] Pearson 757
FFNI_Entitlement FFNI_NeedForAdmiration 0.12 3.24 755 0.001 [ 0.05, 0.19] Pearson 757
FFNI_Entitlement FFNI_ThrillSeeking 0.07 2.02 755 0.043 [ 0.00, 0.14] Pearson 757
FFNI_Exploitativeness FFNI_Indifference -0.06 -1.52 755 0.129 [-0.13, 0.02] Pearson 757
FFNI_Exploitativeness FFNI_LackOfEmpathy 0.24 6.88 755 < .001 [ 0.17, 0.31] Pearson 757
FFNI_Exploitativeness FFNI_Manipulativeness 0.41 12.39 755 < .001 [ 0.35, 0.47] Pearson 757
FFNI_Exploitativeness FFNI_NeedForAdmiration 0.07 2.04 755 0.042 [ 0.00, 0.14] Pearson 757
FFNI_Exploitativeness FFNI_ThrillSeeking 0.06 1.52 755 0.130 [-0.02, 0.13] Pearson 757
FFNI_Indifference FFNI_LackOfEmpathy 0.33 9.67 755 < .001 [ 0.27, 0.39] Pearson 757
FFNI_Indifference FFNI_Manipulativeness 0.14 3.87 755 < .001 [ 0.07, 0.21] Pearson 757
FFNI_Indifference FFNI_NeedForAdmiration -0.46 -14.19 755 < .001 [-0.51, -0.40] Pearson 757
FFNI_Indifference FFNI_ThrillSeeking 0.15 4.21 755 < .001 [ 0.08, 0.22] Pearson 757
FFNI_LackOfEmpathy FFNI_Manipulativeness -0.04 -1.07 755 0.287 [-0.11, 0.03] Pearson 757
FFNI_LackOfEmpathy FFNI_NeedForAdmiration 0.02 0.52 755 0.606 [-0.05, 0.09] Pearson 757
FFNI_LackOfEmpathy FFNI_ThrillSeeking 0.09 2.36 755 0.018 [ 0.01, 0.16] Pearson 757
FFNI_Manipulativeness FFNI_NeedForAdmiration 0.01 0.15 755 0.879 [-0.07, 0.08] Pearson 757
FFNI_Manipulativeness FFNI_ThrillSeeking 0.13 3.60 755 < .001 [ 0.06, 0.20] Pearson 757
FFNI_NeedForAdmiration FFNI_ThrillSeeking 0.05 1.39 755 0.165 [-0.02, 0.12] Pearson 757

Normal Personality (IPIP6)

Parameter1 Parameter2 r t df p 95% CI Method n_Obs
LIE_Ability LIE_Frequency 0.27 7.58 755 < .001 [ 0.20, 0.33] Pearson 757
LIE_Ability LIE_Negativity -0.15 -4.30 755 < .001 [-0.22, -0.08] Pearson 757
LIE_Ability LIE_Contextuality 0.33 9.67 755 < .001 [ 0.27, 0.39] Pearson 757
LIE_Ability IPIP6_Extraversion 0.09 2.39 755 0.017 [ 0.02, 0.16] Pearson 757
LIE_Ability IPIP6_Agreableness 0.01 0.40 755 0.686 [-0.06, 0.09] Pearson 757
LIE_Ability IPIP6_Conscientiousness 0.02 0.47 755 0.642 [-0.05, 0.09] Pearson 757
LIE_Ability IPIP6_Neuroticism -0.04 -1.11 755 0.268 [-0.11, 0.03] Pearson 757
LIE_Ability IPIP6_Openeness 0.15 4.19 755 < .001 [ 0.08, 0.22] Pearson 757
LIE_Ability IPIP6_HonestyHumility -0.11 -2.94 755 0.003 [-0.18, -0.04] Pearson 757
LIE_Frequency LIE_Negativity -0.52 -16.80 755 < .001 [-0.57, -0.47] Pearson 757
LIE_Frequency LIE_Contextuality -0.15 -4.15 755 < .001 [-0.22, -0.08] Pearson 757
LIE_Frequency IPIP6_Extraversion 0.04 1.04 755 0.300 [-0.03, 0.11] Pearson 757
LIE_Frequency IPIP6_Agreableness -0.10 -2.90 755 0.004 [-0.17, -0.03] Pearson 757
LIE_Frequency IPIP6_Conscientiousness -0.11 -2.93 755 0.003 [-0.18, -0.04] Pearson 757
LIE_Frequency IPIP6_Neuroticism 0.06 1.77 755 0.077 [-0.01, 0.13] Pearson 757
LIE_Frequency IPIP6_Openeness -0.08 -2.10 755 0.036 [-0.15, 0.00] Pearson 757
LIE_Frequency IPIP6_HonestyHumility -0.09 -2.59 755 0.010 [-0.16, -0.02] Pearson 757
LIE_Negativity LIE_Contextuality -0.27 -7.81 755 < .001 [-0.34, -0.21] Pearson 757
LIE_Negativity IPIP6_Extraversion -0.08 -2.25 755 0.024 [-0.15, -0.01] Pearson 757
LIE_Negativity IPIP6_Agreableness 0.11 2.98 755 0.003 [ 0.04, 0.18] Pearson 757
LIE_Negativity IPIP6_Conscientiousness 0.08 2.11 755 0.035 [ 0.01, 0.15] Pearson 757
LIE_Negativity IPIP6_Neuroticism 0.02 0.50 755 0.616 [-0.05, 0.09] Pearson 757
LIE_Negativity IPIP6_Openeness -0.04 -1.01 755 0.315 [-0.11, 0.03] Pearson 757
LIE_Negativity IPIP6_HonestyHumility -0.15 -4.09 755 < .001 [-0.22, -0.08] Pearson 757
LIE_Contextuality IPIP6_Extraversion -0.11 -2.93 755 0.004 [-0.18, -0.03] Pearson 757
LIE_Contextuality IPIP6_Agreableness 0.01 0.22 755 0.829 [-0.06, 0.08] Pearson 757
LIE_Contextuality IPIP6_Conscientiousness 0.08 2.31 755 0.021 [ 0.01, 0.15] Pearson 757
LIE_Contextuality IPIP6_Neuroticism 0.01 0.17 755 0.861 [-0.06, 0.08] Pearson 757
LIE_Contextuality IPIP6_Openeness 0.01 0.26 755 0.796 [-0.06, 0.08] Pearson 757
LIE_Contextuality IPIP6_HonestyHumility -0.05 -1.39 755 0.166 [-0.12, 0.02] Pearson 757
IPIP6_Extraversion IPIP6_Agreableness 0.29 8.29 755 < .001 [ 0.22, 0.35] Pearson 757
IPIP6_Extraversion IPIP6_Conscientiousness -0.08 -2.14 755 0.032 [-0.15, -0.01] Pearson 757
IPIP6_Extraversion IPIP6_Neuroticism -0.16 -4.47 755 < .001 [-0.23, -0.09] Pearson 757
IPIP6_Extraversion IPIP6_Openeness 0.11 3.11 755 0.002 [ 0.04, 0.18] Pearson 757
IPIP6_Extraversion IPIP6_HonestyHumility -0.32 -9.30 755 < .001 [-0.38, -0.26] Pearson 757
IPIP6_Agreableness IPIP6_Conscientiousness 0.07 2.06 755 0.040 [ 0.00, 0.15] Pearson 757
IPIP6_Agreableness IPIP6_Neuroticism 0.11 3.15 755 0.002 [ 0.04, 0.18] Pearson 757
IPIP6_Agreableness IPIP6_Openeness 0.23 6.37 755 < .001 [ 0.16, 0.29] Pearson 757
IPIP6_Agreableness IPIP6_HonestyHumility 0.18 5.12 755 < .001 [ 0.11, 0.25] Pearson 757
IPIP6_Conscientiousness IPIP6_Neuroticism -0.16 -4.32 755 < .001 [-0.22, -0.08] Pearson 757
IPIP6_Conscientiousness IPIP6_Openeness -0.08 -2.20 755 0.028 [-0.15, -0.01] Pearson 757
IPIP6_Conscientiousness IPIP6_HonestyHumility 0.02 0.50 755 0.615 [-0.05, 0.09] Pearson 757
IPIP6_Neuroticism IPIP6_Openeness 0.00 -0.06 755 0.950 [-0.07, 0.07] Pearson 757
IPIP6_Neuroticism IPIP6_HonestyHumility -0.20 -5.60 755 < .001 [-0.27, -0.13] Pearson 757
IPIP6_Openeness IPIP6_HonestyHumility 0.10 2.81 755 0.005 [ 0.03, 0.17] Pearson 757

Pathological Personality (PID5)

Parameter1 Parameter2 r t df p 95% CI Method n_Obs
LIE_Ability LIE_Frequency 0.27 7.59 755 < .001 [ 0.20, 0.33] Pearson 757
LIE_Ability LIE_Negativity -0.15 -4.07 755 < .001 [-0.22, -0.08] Pearson 757
LIE_Ability LIE_Contextuality 0.34 10.04 755 < .001 [ 0.28, 0.40] Pearson 757
LIE_Ability PID5_NegativeAffect -0.05 -1.40 755 0.161 [-0.12, 0.02] Pearson 757
LIE_Ability PID5_Detachment -0.05 -1.26 755 0.209 [-0.12, 0.03] Pearson 757
LIE_Ability PID5_Antagonism 0.19 5.42 755 < .001 [ 0.12, 0.26] Pearson 757
LIE_Ability PID5_Disinhibition -0.07 -1.88 755 0.061 [-0.14, 0.00] Pearson 757
LIE_Ability PID5_Psychoticism -0.01 -0.16 755 0.870 [-0.08, 0.07] Pearson 757
LIE_Frequency LIE_Negativity -0.53 -17.26 755 < .001 [-0.58, -0.48] Pearson 757
LIE_Frequency LIE_Contextuality -0.16 -4.40 755 < .001 [-0.23, -0.09] Pearson 757
LIE_Frequency PID5_NegativeAffect 0.04 1.08 755 0.279 [-0.03, 0.11] Pearson 757
LIE_Frequency PID5_Detachment 0.07 1.95 755 0.051 [ 0.00, 0.14] Pearson 757
LIE_Frequency PID5_Antagonism 0.04 1.12 755 0.263 [-0.03, 0.11] Pearson 757
LIE_Frequency PID5_Disinhibition 0.13 3.48 755 < .001 [ 0.05, 0.20] Pearson 757
LIE_Frequency PID5_Psychoticism 0.00 0.10 755 0.917 [-0.07, 0.08] Pearson 757
LIE_Negativity LIE_Contextuality -0.27 -7.78 755 < .001 [-0.34, -0.21] Pearson 757
LIE_Negativity PID5_NegativeAffect 0.11 2.95 755 0.003 [ 0.04, 0.18] Pearson 757
LIE_Negativity PID5_Detachment -0.01 -0.25 755 0.801 [-0.08, 0.06] Pearson 757
LIE_Negativity PID5_Antagonism -0.02 -0.47 755 0.642 [-0.09, 0.05] Pearson 757
LIE_Negativity PID5_Disinhibition -0.04 -1.17 755 0.241 [-0.11, 0.03] Pearson 757
LIE_Negativity PID5_Psychoticism 0.02 0.44 755 0.662 [-0.06, 0.09] Pearson 757
LIE_Contextuality PID5_NegativeAffect 0.08 2.29 755 0.022 [ 0.01, 0.15] Pearson 757
LIE_Contextuality PID5_Detachment 0.03 0.80 755 0.425 [-0.04, 0.10] Pearson 757
LIE_Contextuality PID5_Antagonism -0.09 -2.51 755 0.012 [-0.16, -0.02] Pearson 757
LIE_Contextuality PID5_Disinhibition -0.06 -1.66 755 0.098 [-0.13, 0.01] Pearson 757
LIE_Contextuality PID5_Psychoticism 0.06 1.67 755 0.095 [-0.01, 0.13] Pearson 757
PID5_NegativeAffect PID5_Detachment 0.17 4.81 755 < .001 [ 0.10, 0.24] Pearson 757
PID5_NegativeAffect PID5_Antagonism 0.14 3.87 755 < .001 [ 0.07, 0.21] Pearson 757
PID5_NegativeAffect PID5_Disinhibition 0.16 4.34 755 < .001 [ 0.09, 0.22] Pearson 757
PID5_NegativeAffect PID5_Psychoticism 0.25 6.96 755 < .001 [ 0.18, 0.31] Pearson 757
PID5_Detachment PID5_Antagonism 0.13 3.69 755 < .001 [ 0.06, 0.20] Pearson 757
PID5_Detachment PID5_Disinhibition 0.07 1.94 755 0.053 [ 0.00, 0.14] Pearson 757
PID5_Detachment PID5_Psychoticism 0.26 7.42 755 < .001 [ 0.19, 0.33] Pearson 757
PID5_Antagonism PID5_Disinhibition 0.22 6.26 755 < .001 [ 0.15, 0.29] Pearson 757
PID5_Antagonism PID5_Psychoticism 0.13 3.48 755 < .001 [ 0.05, 0.20] Pearson 757
PID5_Disinhibition PID5_Psychoticism 0.36 10.44 755 < .001 [ 0.29, 0.42] Pearson 757

Light Triad (LTS)

Parameter1 Parameter2 r t df p 95% CI Method n_Obs
LIE_Ability LIE_Frequency 0.27 7.84 755 < .001 [ 0.21, 0.34] Pearson 757
LIE_Ability LIE_Negativity -0.16 -4.39 755 < .001 [-0.23, -0.09] Pearson 757
LIE_Ability LIE_Contextuality 0.33 9.47 755 < .001 [ 0.26, 0.39] Pearson 757
LIE_Ability LTS_FaithInHumanity 0.01 0.15 755 0.878 [-0.07, 0.08] Pearson 757
LIE_Ability LTS_Humanism -0.04 -1.03 755 0.302 [-0.11, 0.03] Pearson 757
LIE_Ability LTS_Kantianism 0.01 0.26 755 0.794 [-0.06, 0.08] Pearson 757
LIE_Frequency LIE_Negativity -0.52 -16.92 755 < .001 [-0.57, -0.47] Pearson 757
LIE_Frequency LIE_Contextuality -0.16 -4.32 755 < .001 [-0.22, -0.09] Pearson 757
LIE_Frequency LTS_FaithInHumanity -0.11 -3.00 755 0.003 [-0.18, -0.04] Pearson 757
LIE_Frequency LTS_Humanism 0.03 0.88 755 0.381 [-0.04, 0.10] Pearson 757
LIE_Frequency LTS_Kantianism 0.09 2.55 755 0.011 [ 0.02, 0.16] Pearson 757
LIE_Negativity LIE_Contextuality -0.28 -8.10 755 < .001 [-0.35, -0.22] Pearson 757
LIE_Negativity LTS_FaithInHumanity -0.06 -1.70 755 0.090 [-0.13, 0.01] Pearson 757
LIE_Negativity LTS_Humanism -0.06 -1.77 755 0.078 [-0.13, 0.01] Pearson 757
LIE_Negativity LTS_Kantianism -0.12 -3.19 755 0.001 [-0.19, -0.04] Pearson 757
LIE_Contextuality LTS_FaithInHumanity 0.00 0.12 755 0.906 [-0.07, 0.08] Pearson 757
LIE_Contextuality LTS_Humanism -0.10 -2.72 755 0.007 [-0.17, -0.03] Pearson 757
LIE_Contextuality LTS_Kantianism -0.04 -1.08 755 0.281 [-0.11, 0.03] Pearson 757
LTS_FaithInHumanity LTS_Humanism 0.47 14.76 755 < .001 [ 0.42, 0.53] Pearson 757
LTS_FaithInHumanity LTS_Kantianism 0.16 4.49 755 < .001 [ 0.09, 0.23] Pearson 757
LTS_Humanism LTS_Kantianism 0.28 7.90 755 < .001 [ 0.21, 0.34] Pearson 757

Impulsivity (UPPS)

Parameter1 Parameter2 r t df p 95% CI Method n_Obs
LIE_Ability LIE_Frequency 0.28 7.90 755 < .001 [ 0.21, 0.34] Pearson 757
LIE_Ability LIE_Negativity -0.16 -4.58 755 < .001 [-0.23, -0.09] Pearson 757
LIE_Ability LIE_Contextuality 0.31 9.00 755 < .001 [ 0.25, 0.37] Pearson 757
LIE_Ability UPPS_NegativeUrgency 0.02 0.60 755 0.546 [-0.05, 0.09] Pearson 757
LIE_Ability UPPS_PositiveUrgency -0.04 -1.18 755 0.238 [-0.11, 0.03] Pearson 757
LIE_Ability UPPS_LackOfPerseverance -0.02 -0.46 755 0.643 [-0.09, 0.05] Pearson 757
LIE_Ability UPPS_LackOfPremeditation -0.04 -1.03 755 0.301 [-0.11, 0.03] Pearson 757
LIE_Ability UPPS_SensationSeeking 0.07 2.04 755 0.041 [ 0.00, 0.14] Pearson 757
LIE_Frequency LIE_Negativity -0.52 -16.81 755 < .001 [-0.57, -0.47] Pearson 757
LIE_Frequency LIE_Contextuality -0.14 -3.93 755 < .001 [-0.21, -0.07] Pearson 757
LIE_Frequency UPPS_NegativeUrgency 0.06 1.76 755 0.079 [-0.01, 0.13] Pearson 757
LIE_Frequency UPPS_PositiveUrgency 0.16 4.38 755 < .001 [ 0.09, 0.23] Pearson 757
LIE_Frequency UPPS_LackOfPerseverance 0.04 1.17 755 0.241 [-0.03, 0.11] Pearson 757
LIE_Frequency UPPS_LackOfPremeditation 0.01 0.41 755 0.683 [-0.06, 0.09] Pearson 757
LIE_Frequency UPPS_SensationSeeking 0.00 0.11 755 0.914 [-0.07, 0.08] Pearson 757
LIE_Negativity LIE_Contextuality -0.29 -8.29 755 < .001 [-0.35, -0.22] Pearson 757
LIE_Negativity UPPS_NegativeUrgency 0.10 2.75 755 0.006 [ 0.03, 0.17] Pearson 757
LIE_Negativity UPPS_PositiveUrgency 0.03 0.71 755 0.476 [-0.05, 0.10] Pearson 757
LIE_Negativity UPPS_LackOfPerseverance -0.08 -2.15 755 0.032 [-0.15, -0.01] Pearson 757
LIE_Negativity UPPS_LackOfPremeditation -0.15 -4.05 755 < .001 [-0.21, -0.08] Pearson 757
LIE_Negativity UPPS_SensationSeeking 0.03 0.82 755 0.412 [-0.04, 0.10] Pearson 757
LIE_Contextuality UPPS_NegativeUrgency 0.03 0.84 755 0.402 [-0.04, 0.10] Pearson 757
LIE_Contextuality UPPS_PositiveUrgency -0.01 -0.29 755 0.770 [-0.08, 0.06] Pearson 757
LIE_Contextuality UPPS_LackOfPerseverance -0.08 -2.09 755 0.037 [-0.15, 0.00] Pearson 757
LIE_Contextuality UPPS_LackOfPremeditation -0.10 -2.84 755 0.005 [-0.17, -0.03] Pearson 757
LIE_Contextuality UPPS_SensationSeeking 0.07 2.00 755 0.046 [ 0.00, 0.14] Pearson 757
UPPS_NegativeUrgency UPPS_PositiveUrgency 0.58 19.81 755 < .001 [ 0.54, 0.63] Pearson 757
UPPS_NegativeUrgency UPPS_LackOfPerseverance -0.01 -0.33 755 0.738 [-0.08, 0.06] Pearson 757
UPPS_NegativeUrgency UPPS_LackOfPremeditation 0.04 1.21 755 0.225 [-0.03, 0.12] Pearson 757
UPPS_NegativeUrgency UPPS_SensationSeeking -0.14 -3.82 755 < .001 [-0.21, -0.07] Pearson 757
UPPS_PositiveUrgency UPPS_LackOfPerseverance 0.01 0.24 755 0.812 [-0.06, 0.08] Pearson 757
UPPS_PositiveUrgency UPPS_LackOfPremeditation 0.21 5.81 755 < .001 [ 0.14, 0.27] Pearson 757
UPPS_PositiveUrgency UPPS_SensationSeeking 0.26 7.27 755 < .001 [ 0.19, 0.32] Pearson 757
UPPS_LackOfPerseverance UPPS_LackOfPremeditation 0.38 11.31 755 < .001 [ 0.32, 0.44] Pearson 757
UPPS_LackOfPerseverance UPPS_SensationSeeking -0.13 -3.73 755 < .001 [-0.20, -0.06] Pearson 757
UPPS_LackOfPremeditation UPPS_SensationSeeking 0.03 0.76 755 0.445 [-0.04, 0.10] Pearson 757

Emotion Regulation (DERS)

Parameter1 Parameter2 r t df p 95% CI Method n_Obs
LIE_Ability LIE_Frequency 0.28 7.92 755 < .001 [ 0.21, 0.34] Pearson 757
LIE_Ability LIE_Negativity -0.15 -4.26 755 < .001 [-0.22, -0.08] Pearson 757
LIE_Ability LIE_Contextuality 0.33 9.50 755 < .001 [ 0.26, 0.39] Pearson 757
LIE_Ability DERS_Awareness -0.06 -1.67 755 0.096 [-0.13, 0.01] Pearson 757
LIE_Ability DERS_Clarity -0.06 -1.78 755 0.075 [-0.14, 0.01] Pearson 757
LIE_Ability DERS_Goals -0.04 -1.18 755 0.240 [-0.11, 0.03] Pearson 757
LIE_Ability DERS_Impulse 0.03 0.93 755 0.351 [-0.04, 0.10] Pearson 757
LIE_Ability DERS_NonAcceptance 0.00 0.08 755 0.938 [-0.07, 0.07] Pearson 757
LIE_Ability DERS_Strategies 0.01 0.18 755 0.859 [-0.06, 0.08] Pearson 757
LIE_Frequency LIE_Negativity -0.56 -18.46 755 < .001 [-0.60, -0.51] Pearson 757
LIE_Frequency LIE_Contextuality -0.15 -4.11 755 < .001 [-0.22, -0.08] Pearson 757
LIE_Frequency DERS_Awareness 0.04 1.04 755 0.297 [-0.03, 0.11] Pearson 757
LIE_Frequency DERS_Clarity 0.10 2.85 755 0.005 [ 0.03, 0.17] Pearson 757
LIE_Frequency DERS_Goals -0.06 -1.76 755 0.078 [-0.13, 0.01] Pearson 757
LIE_Frequency DERS_Impulse 0.13 3.47 755 < .001 [ 0.05, 0.19] Pearson 757
LIE_Frequency DERS_NonAcceptance 0.09 2.58 755 0.010 [ 0.02, 0.16] Pearson 757
LIE_Frequency DERS_Strategies 0.01 0.17 755 0.861 [-0.06, 0.08] Pearson 757
LIE_Negativity LIE_Contextuality -0.26 -7.46 755 < .001 [-0.33, -0.19] Pearson 757
LIE_Negativity DERS_Awareness -0.06 -1.60 755 0.111 [-0.13, 0.01] Pearson 757
LIE_Negativity DERS_Clarity 0.02 0.58 755 0.563 [-0.05, 0.09] Pearson 757
LIE_Negativity DERS_Goals -0.04 -1.10 755 0.273 [-0.11, 0.03] Pearson 757
LIE_Negativity DERS_Impulse 0.06 1.70 755 0.090 [-0.01, 0.13] Pearson 757
LIE_Negativity DERS_NonAcceptance 0.11 3.08 755 0.002 [ 0.04, 0.18] Pearson 757
LIE_Negativity DERS_Strategies 0.00 0.07 755 0.943 [-0.07, 0.07] Pearson 757
LIE_Contextuality DERS_Awareness -0.05 -1.39 755 0.166 [-0.12, 0.02] Pearson 757
LIE_Contextuality DERS_Clarity -0.03 -0.74 755 0.462 [-0.10, 0.04] Pearson 757
LIE_Contextuality DERS_Goals 0.03 0.79 755 0.428 [-0.04, 0.10] Pearson 757
LIE_Contextuality DERS_Impulse -0.01 -0.40 755 0.689 [-0.09, 0.06] Pearson 757
LIE_Contextuality DERS_NonAcceptance 0.01 0.18 755 0.856 [-0.06, 0.08] Pearson 757
LIE_Contextuality DERS_Strategies 0.02 0.43 755 0.670 [-0.06, 0.09] Pearson 757
DERS_Awareness DERS_Clarity 0.28 7.93 755 < .001 [ 0.21, 0.34] Pearson 757
DERS_Awareness DERS_Goals -0.19 -5.39 755 < .001 [-0.26, -0.12] Pearson 757
DERS_Awareness DERS_Impulse 0.06 1.53 755 0.125 [-0.02, 0.13] Pearson 757
DERS_Awareness DERS_NonAcceptance -0.05 -1.47 755 0.143 [-0.12, 0.02] Pearson 757
DERS_Awareness DERS_Strategies 0.02 0.43 755 0.669 [-0.06, 0.09] Pearson 757
DERS_Clarity DERS_Goals 0.03 0.82 755 0.410 [-0.04, 0.10] Pearson 757
DERS_Clarity DERS_Impulse 0.10 2.63 755 0.009 [ 0.02, 0.17] Pearson 757
DERS_Clarity DERS_NonAcceptance 0.17 4.79 755 < .001 [ 0.10, 0.24] Pearson 757
DERS_Clarity DERS_Strategies 0.20 5.61 755 < .001 [ 0.13, 0.27] Pearson 757
DERS_Goals DERS_Impulse 0.29 8.22 755 < .001 [ 0.22, 0.35] Pearson 757
DERS_Goals DERS_NonAcceptance 0.17 4.83 755 < .001 [ 0.10, 0.24] Pearson 757
DERS_Goals DERS_Strategies 0.23 6.64 755 < .001 [ 0.17, 0.30] Pearson 757
DERS_Impulse DERS_NonAcceptance 0.07 1.79 755 0.074 [-0.01, 0.14] Pearson 757
DERS_Impulse DERS_Strategies 0.46 14.29 755 < .001 [ 0.40, 0.52] Pearson 757
DERS_NonAcceptance DERS_Strategies 0.22 6.19 755 < .001 [ 0.15, 0.29] Pearson 757

Interoception (MAIA2)

Parameter1 Parameter2 r t df p 95% CI Method n_Obs
LIE_Ability LIE_Frequency 0.28 7.91 755 < .001 [ 0.21, 0.34] Pearson 757
LIE_Ability LIE_Negativity -0.16 -4.52 755 < .001 [-0.23, -0.09] Pearson 757
LIE_Ability LIE_Contextuality 0.33 9.64 755 < .001 [ 0.27, 0.39] Pearson 757
LIE_Ability MAIA2_Noticing 0.08 2.17 755 0.030 [ 0.01, 0.15] Pearson 757
LIE_Ability MAIA2_BodyListening -0.01 -0.38 755 0.703 [-0.09, 0.06] Pearson 757
LIE_Frequency LIE_Negativity -0.54 -17.86 755 < .001 [-0.59, -0.49] Pearson 757
LIE_Frequency LIE_Contextuality -0.17 -4.62 755 < .001 [-0.23, -0.10] Pearson 757
LIE_Frequency MAIA2_Noticing -0.07 -1.86 755 0.063 [-0.14, 0.00] Pearson 757
LIE_Frequency MAIA2_BodyListening 0.10 2.89 755 0.004 [ 0.03, 0.17] Pearson 757
LIE_Negativity LIE_Contextuality -0.27 -7.58 755 < .001 [-0.33, -0.20] Pearson 757
LIE_Negativity MAIA2_Noticing 0.05 1.36 755 0.173 [-0.02, 0.12] Pearson 757
LIE_Negativity MAIA2_BodyListening 0.04 1.03 755 0.305 [-0.03, 0.11] Pearson 757
LIE_Contextuality MAIA2_Noticing 0.00 0.03 755 0.975 [-0.07, 0.07] Pearson 757
LIE_Contextuality MAIA2_BodyListening 0.04 1.10 755 0.274 [-0.03, 0.11] Pearson 757
MAIA2_Noticing MAIA2_BodyListening 0.56 18.37 755 < .001 [ 0.50, 0.60] Pearson 757

References

References
Daniel Lüdecke and Dominique Makowski (NA). easystats: Jump in the easyverse. R package version 0.2.0. https://github.com/easystats/easystats
Daniel Lüdecke, Dominique Makowski and Philip Waggoner (NA). performance: Assessment of Regression Models Performance. R package version 0.4.4.1. https://easystats.github.io/performance/
Daniel Lüdecke, Dominique Makowski, Philip Waggoner and Mattan S. Ben-Shachar (NA). see: Visualisation Toolbox for ‘easystats’ and Extra Geoms, Themes and Color Palettes for ‘ggplot2’. R package version 0.4.0.1. https://easystats.github.io/see/
Dirk Eddelbuettel and Romain Francois (2011). Rcpp: Seamless R and C++ Integration. Journal of Statistical Software, 40(8), 1-18. URL http://www.jstatsoft.org/v40/i08/.
Dominique Makowski and Daniel Lüdecke (NA). correlation: Easy Peasy Correlations. R package version 0.1.0. https://github.com/easystats/correlation
Dominique Makowski, Daniel Lüdecke and Mattan S. Ben-Shachar (NA). modelbased: Estimation of Model-Based Predictions, Contrasts and Means. R package version 0.1.0. https://github.com/easystats/modelbased
Goodrich B, Gabry J, Ali I & Brilleman S. (2018). rstanarm: Bayesian applied regression modeling via Stan. R package version 2.17.4. http://mc-stan.org/.
H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
Hadley Wickham (2019). forcats: Tools for Working with Categorical Variables (Factors). R package version 0.4.0. https://CRAN.R-project.org/package=forcats
Hadley Wickham (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.4.0. https://CRAN.R-project.org/package=stringr
Hadley Wickham and Lionel Henry (2019). tidyr: Tidy Messy Data. R package version 1.0.0. https://CRAN.R-project.org/package=tidyr
Hadley Wickham, Jim Hester and Romain Francois (2018). readr: Read Rectangular Text Data. R package version 1.3.1. https://CRAN.R-project.org/package=readr
Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2019). dplyr: A Grammar of Data Manipulation. R package version 0.8.3. https://CRAN.R-project.org/package=dplyr
Kirill Müller and Hadley Wickham (2019). tibble: Simple Data Frames. R package version 2.1.3. https://CRAN.R-project.org/package=tibble
Kun Ren and Kenton Russell (2016). formattable: Create ‘Formattable’ Data Structures. R package version 0.2.0.1. https://CRAN.R-project.org/package=formattable
Lionel Henry and Hadley Wickham (2019). purrr: Functional Programming Tools. R package version 0.3.3. https://CRAN.R-project.org/package=purrr
Lüdecke D, Waggoner P, Makowski D (2019). “insight: A Unified Interfaceto Access Information from Model Objects in R.” Journal of Open SourceSoftware, 4(38), 1412. doi: 10.21105/joss.01412 (URL:https://doi.org/10.21105/joss.01412).
Makowski, Ben-Shachar & Lüdecke (2019). Compute and interpret indices of effect size. CRAN. Available from https://github.com/easystats/effectsize.
Makowski, Ben-Shachar & Lüdecke (2019). Describe and understand your model’s parameters. CRAN. Available from https://github.com/easystats/parameters.
Makowski, D. & Lüdecke, D. (2019). The report package for R: Ensuring the use of best practices for results reporting. CRAN. Available from https://github.com/easystats/report. doi: .
Makowski, D., Ben-Shachar, M., & Lüdecke, D. (2019). bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework. Journal of Open Source Software, 4(40), 1541. doi:10.21105/joss.01541
Revelle, W. (2019) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 1.9.12.
Thomas Lin Pedersen (2019). ggforce: Accelerating ‘ggplot2’. R package version 0.3.1. https://CRAN.R-project.org/package=ggforce
Thomas Lin Pedersen (2019). ggraph: An Implementation of Grammar of Graphics for Graphs and Networks. R package version 2.0.0. https://CRAN.R-project.org/package=ggraph
Thomas Lin Pedersen (2019). tidygraph: A Tidy API for Graph Manipulation. R package version 1.1.2. https://CRAN.R-project.org/package=tidygraph
Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686
Yihui Xie (2020). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.28.